500_83_get_mixed_effect_model_results.R
paul
2020-02-04
Preamble
This code commentary is included in the R code itself and can be rendered at any stage using rmarkdown::render ("/Users/paul/Documents/CU_combined/Github/500_83_get_mixed_effect_model_results.R", clean = TRUE, output_format = "html_notebook"). Please check the session info at the end of the document for further notes on the coding environment.
Environment preparation
Empty buffer.
rm(list=ls())Load Packages
library ("tidyverse") # dplyr and friends
library ("ggplot2") # for ggCaterpillar
library ("gdata") # matrix functions
library ("reshape2") # melting
library ("lme4") # mixed effect model
library ("sjPlot") # mixed effect model - with plotting
library ("cowplot") # exporting ggplots
library ("formula.tools") # better formatting of formulas
library ("stringr") # better string concatenation
library ("magrittr") # back-piping (only used for type conversion)Functions
# Loaded from helper script:
source("/Users/paul/Documents/CU_combined/Github/500_00_functions.R")“Not in” function
`%!in%` = Negate(`%in%`)Function to subset data to fit model variables. Currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
match_data_to_formula <- function (formula_item, data_item){
# package loading
require ("tidyverse")
# message
message("\nData is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.")
# Setting types
# for debugging only
# print(head(data_item))
message("- Setting types.")
cols <- c("PORT", "DEST", "ECO_PORT", "ECO_DEST", "ECO_DIFF")
data_item[cols] <- lapply(data_item[cols], as.factor)
# for debugging only
# print(head(data_item))
# remove superflous columns
vars_to_keep <- all.vars (formula_item)
message("- Input dimensions are: ", paste0( (dim(data_item)), " "), ".")
message("- Removed variables are: ", paste0( names(data_item)[which(names(data_item) %!in% vars_to_keep)], " "), ".")
message("- Kept variables are: ", paste0(vars_to_keep, " "), ".")
data_item <- data_item %>% select(all_of(vars_to_keep))
message("- Intermediate dimensions are: ", paste0( (dim(data_item)), " "), ".")
# remove superflous rows
message("- Undefined rows have been removed, assuming they were real \"NA\" and not \"0\".")
data_item <- data_item %>% filter(complete.cases(.))
message("- Final dimensions are: ", paste0( (dim(data_item)), " "), ".")
# return table object suitable for modelling with model formula
return(data_item)
}Calculate random effect model results
calculate_model <- function(formula_item, data_item){
message("\nModelling function received variables: ", paste0(names(data_item) , " "), ".")
message(" ... dimensions: ", paste0( (dim(data_item)), " "), ".")
message(" ... formula: ", paste0(formula_item , " "), "." )
model <- lmer(formula_item, data = data_item, REML=FALSE)
return(model)
}Model definitions
Define full models
following https://stackoverflow.com/questions/25312818/using-lapply-to-fit-multiple-model-how-to-keep-the-model-formula-self-contain
full_formulae <- list(
# Original by Paul
as.formula(RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
# as per email 04.02.2020
# Unifrac ~ VOY_FREQ + env similarity + ecoregion + random port effects
as.formula(RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
# Unifrac ~ B_FON_NOECO + env similarity + ecoregion + random port effects
as.formula(RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
# Unifrac ~ B_HON_NOECO + env similarity + ecoregion + random port effects
as.formula(RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST))
)Define null models
For Anova comparison. Order must be the same as in list full_models.
null_formulae <- list(
# Original by Paul
as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
# as per email 04.02.2020
# Unifrac ~ VOY_FREQ + env similarity + ecoregion + random port effects
as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
# Unifrac ~ B_FON_NOECO + env similarity + ecoregion + random port effects
as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
# Unifrac ~ B_HON_NOECO + env similarity + ecoregion + random port effects
as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST))
)Read in and format data
Please refer to project README.md file for further details on previous processing steps (dated 31-Jan-2020).
# define file path components for listing
model_input_folder <- "/Users/paul/Documents/CU_combined/Zenodo/Results"
model_input_pattern <- glob2rx("??_results_euk_*_model_data_*.csv")
# read all file into lists for `lapply()` usage
model_input_files <- list.files(path=model_input_folder,
pattern = model_input_pattern, full.names = TRUE)
# store all tables in list and save input filenames alongside - skipping "X1"
# in case previous tables have column numbers, which they should not have anymore.
model_input_data <- suppressWarnings(lapply(model_input_files,
function(listed_file) read_csv(listed_file, col_types = cols('X1' = col_skip()))))
names(model_input_data) <- model_input_filesObtaining modelling results
Initialize results table
So that it can be filled in the loop.
analysis_summaries <- expand.grid(seq(model_input_data), seq(full_formulae))
analysis_summaries <- as_tibble(analysis_summaries)
analysis_summaries <- setNames(analysis_summaries, c("DIDX", "FIDX"))
analysis_summaries <- analysis_summaries %>% add_column(AKAI = 0, PVAL = 0, FRML = 0, DATA = 0)
analysis_summaries$AKAI %<>% as.double
analysis_summaries$DATA %<>% as.character
analysis_summaries$FRML %<>% as.character
analysis_summaries$PVAL %<>% as.double
# use this approach to get around the loop - later
# define all possible combinations for mapply call
# for later - starting point
# analysis_combinations <- expand.grid(seq(model_input_data), seq(full_formulae))
# setNames(analysis_combinations, c("model_index", "formula_index"))
# for later - starting point
# list(model_input_data, full_formulae)Calculating Results
Initially using loops, for sanity reasons. While looping fill results table analysis_summaries. Check raw model outputs below for Writing above results to results table row: n and look up n in both results tables all the way at the end of this page.
# loop over formulae
for (i in seq(full_formulae)){
# loop over dat sets
for (j in seq(model_input_data)){
message("°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ ")
message("\nStarting new analysis, with data index DIDX \"", j , "\" and formula index FIDX \"", i, "\" in Summary Tables." )
message("Using formula: ", as.character(full_formulae[[i]]), " with data: ", as.character(basename(names(model_input_data)[[j]])), ". ")
# define current model formula for parsing
full_formula <- full_formulae[[i]]
null_formula <- null_formulae[[i]]
# define current data table for subsetting
model_data_raw <- model_input_data[[j]]
# match input table dimensions to current model formulae
model_data <- match_data_to_formula(full_formula, model_data_raw)
print(model_data, n = Inf)
# calculate full model
full_model <- calculate_model(full_formula, model_data)
# calculate null model
null_model <- calculate_model(null_formula, model_data)
# print model summary and evaluations
message("\nGetting Model Summary: ")
sm <- summary(full_model)
print(sm)
message("\nGetting Model Coefficients from Summary: ")
print(sm$coefficients)
message("\nGetting Model ANOVA: ")
an <- try(anova(null_model, full_model))
try(print(an))
# plot model coefficients
message("\nPlotting Model Coefficients: ")
plot <- plot_model(full_model, show.values = TRUE, value.offset = .3,
type = "std",
title = paste("Coefficients for formula \"", as.character(full_formula),
"\" and variables \"", str_c(names(model_data), collapse = "\", \""),"\" of input file: \"",
basename(names(model_input_data)[[j]]), "\"." ))
print(plot)
# gather results
# set current row of results table
crnt_row <- intersect(which(analysis_summaries$DIDX == j), which(analysis_summaries$FIDX == i))
# message("Writing above results to results table row (but the table is re-sorted): ", crnt_row)
# fill results table
analysis_summaries[crnt_row, ]$AKAI <- extractAIC(full_model)[2]
analysis_summaries[crnt_row, ]$DATA <- as.character(basename(names(model_input_data)[[j]]))
analysis_summaries[crnt_row, ]$FRML <- as.character(full_formulae[[i]])
analysis_summaries[crnt_row, ]$PVAL <- an[2,8]
# keep in mind for further elements from anova object:
# > str(an)
# Classes ‘anova’ and 'data.frame': 2 obs. of 8 variables:
# $ Df : num 6 7
# $ AIC : num -158 -159
# $ BIC : num -145 -144
# $ logLik : num 84.8 86.5
# $ deviance : num -170 -173
# $ Chisq : num NA 3.49
# $ Chi Df : num NA 1
# $ Pr(>Chisq): num NA 0.0617
}
}°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "1" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 64 6 .# A tibble: 64 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.836 10 1.45 TRUE AD BT
2 0.924 6 1.96 TRUE AD HT
3 0.880 2 2.15 TRUE AD WL
4 0.700 15 1.06 TRUE AW WL
5 0.636 189 1.56 TRUE BT AW
6 0.695 20 1.50 TRUE BT GH
7 0.818 11 2.92 TRUE BT HN
8 0.794 287 1.52 FALSE BT HT
9 0.785 26 2.14 TRUE BT LB
10 0.756 75 3.16 FALSE BT MI
11 0.794 221 1.56 FALSE BT NO
12 0.690 6 1.50 TRUE BT OK
13 0.772 17 1.41 TRUE BT PL
14 0.665 5 1.49 TRUE BT RC
15 0.732 83 1.57 TRUE BT RT
16 0.723 180 0.921 FALSE BT WL
17 0.776 94 2.25 TRUE BT ZB
18 0.832 22 1.29 FALSE CB PL
19 0.683 11 0.547 FALSE CB RC
20 0.782 2 1.22 TRUE CB RT
21 0.654 11 1.07 TRUE GH WL
22 0.739 30 2.79 TRUE HN CB
23 0.829 30 2.81 TRUE HN HT
24 0.742 7 0.657 TRUE HN MI
25 0.774 316 2.11 TRUE HT AW
26 0.747 44 2.09 TRUE HT GH
27 0.885 93 2.53 TRUE HT LB
28 0.845 429 2.94 FALSE HT MI
29 0.628 3937 0.0459 FALSE HT NO
30 0.819 3 1.58 TRUE HT OK
31 0.639 21 1.88 TRUE HT PL
32 0.828 4 2.74 TRUE HT PM
33 0.824 37 1.88 TRUE HT RC
34 0.695 498 2.23 TRUE HT RT
35 0.639 31 1.55 FALSE HT WL
36 0.869 16 3.39 TRUE HT ZB
37 0.738 74 1.94 FALSE LB CB
38 0.726 11 1.50 TRUE LB MI
39 0.844 3 2.92 TRUE LB WL
40 0.748 114 4.15 TRUE MI AW
41 0.864 185 2.94 FALSE MI NO
42 0.712 8 3.38 TRUE MI OK
43 0.786 44 4.18 TRUE MI RT
44 0.799 2 3.49 TRUE MI ZB
45 0.650 11 1.59 FALSE NO WL
46 0.603 8 1.12 TRUE RT WL
47 0.815 556 2.06 TRUE SI AD
48 0.740 622 4.01 TRUE SI AW
49 0.748 142 3.18 TRUE SI BT
50 0.724 18 3.18 TRUE SI CB
51 0.789 77 3.93 TRUE SI GH
52 0.762 182 0.576 TRUE SI HN
53 0.836 435 2.81 TRUE SI HT
54 0.730 395 1.55 TRUE SI LB
55 0.688 24 0.506 TRUE SI MI
56 0.853 383 2.80 TRUE SI NO
57 0.692 126 3.17 TRUE SI OK
58 0.835 112 3.86 TRUE SI PL
59 0.780 13 2.54 TRUE SI PM
60 0.691 60 2.94 TRUE SI RC
61 0.774 1055 4.05 TRUE SI RT
62 0.798 12 3.84 TRUE SI WL
63 0.789 207 3.51 TRUE SI ZB
64 0.817 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-159.1 -143.9 86.5 -173.1 57
Scaled residuals:
Min 1Q Median 3Q Max
-2.68550 -0.56009 0.01728 0.63958 1.55677
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001079 0.03285
PORT (Intercept) 0.001352 0.03677
Residual 0.002530 0.05030
Number of obs: 64, groups: DEST, 17; PORT, 13
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.022e-01 2.936e-02 23.917
PRED_TRIPS -3.087e-05 1.531e-05 -2.016
PRED_ENV 3.222e-02 8.089e-03 3.983
ECO_DIFFTRUE -2.098e-03 2.103e-02 -0.100
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.338
PRED_ENV -0.565 0.216
ECO_DIFFTRU -0.594 0.249 -0.030
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.7021552235 2.935840e-02 23.91667141
PRED_TRIPS -0.0000308742 1.531312e-05 -2.01619301
PRED_ENV 0.0322170601 8.088774e-03 3.98293467
ECO_DIFFTRUE -0.0020984868 2.103275e-02 -0.09977234
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -157.57 -144.61 84.783 -169.57
full_model 7 -159.06 -143.94 86.528 -173.06 3.4905 1 0.06172 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "2" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 69 6 .# A tibble: 69 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.836 10 1.45 TRUE AD BT
2 0.924 6 1.96 TRUE AD HT
3 0.880 2 2.15 TRUE AD WL
4 0.700 15 1.06 TRUE AW WL
5 0.636 189 1.56 TRUE BT AW
6 0.695 20 1.50 TRUE BT GH
7 0.818 11 2.92 TRUE BT HN
8 0.794 287 1.52 FALSE BT HT
9 0.785 26 2.14 TRUE BT LB
10 0.756 75 3.16 FALSE BT MI
11 0.794 221 1.56 FALSE BT NO
12 0.690 6 1.50 TRUE BT OK
13 0.772 17 1.41 TRUE BT PL
14 0.665 5 1.49 TRUE BT RC
15 0.732 83 1.57 TRUE BT RT
16 0.723 180 0.921 FALSE BT WL
17 0.776 94 2.25 TRUE BT ZB
18 0.832 22 1.29 FALSE CB PL
19 0.683 11 0.547 FALSE CB RC
20 0.782 2 1.22 TRUE CB RT
21 0.654 11 1.07 TRUE GH WL
22 0.739 30 2.79 TRUE HN CB
23 0.829 30 2.81 TRUE HN HT
24 0.742 7 0.657 TRUE HN MI
25 0.774 316 2.11 TRUE HT AW
26 0.747 44 2.09 TRUE HT GH
27 0.885 93 2.53 TRUE HT LB
28 0.845 429 2.94 FALSE HT MI
29 0.628 3937 0.0459 FALSE HT NO
30 0.819 3 1.58 TRUE HT OK
31 0.639 21 1.88 TRUE HT PL
32 0.828 4 2.74 TRUE HT PM
33 0.824 37 1.88 TRUE HT RC
34 0.695 498 2.23 TRUE HT RT
35 0.639 31 1.55 FALSE HT WL
36 0.869 16 3.39 TRUE HT ZB
37 0.738 74 1.94 FALSE LB CB
38 0.726 11 1.50 TRUE LB MI
39 0.844 3 2.92 TRUE LB WL
40 0.748 114 4.15 TRUE MI AW
41 0.864 185 2.94 FALSE MI NO
42 0.712 8 3.38 TRUE MI OK
43 0.786 44 4.18 TRUE MI RT
44 0.799 2 3.49 TRUE MI ZB
45 0.650 11 1.59 FALSE NO WL
46 0.775 0 2.92 TRUE PH BT
47 0.700 0 2.79 TRUE PH CB
48 0.861 0 2.81 TRUE PH HT
49 0.658 0 0.657 TRUE PH MI
50 0.706 0 0.576 TRUE PH SI
51 0.603 8 1.12 TRUE RT WL
52 0.815 556 2.06 TRUE SI AD
53 0.740 622 4.01 TRUE SI AW
54 0.748 142 3.18 TRUE SI BT
55 0.724 18 3.18 TRUE SI CB
56 0.789 77 3.93 TRUE SI GH
57 0.762 182 0.576 TRUE SI HN
58 0.836 435 2.81 TRUE SI HT
59 0.730 395 1.55 TRUE SI LB
60 0.688 24 0.506 TRUE SI MI
61 0.853 383 2.80 TRUE SI NO
62 0.692 126 3.17 TRUE SI OK
63 0.835 112 3.86 TRUE SI PL
64 0.780 13 2.54 TRUE SI PM
65 0.691 60 2.94 TRUE SI RC
66 0.774 1055 4.05 TRUE SI RT
67 0.798 12 3.84 TRUE SI WL
68 0.789 207 3.51 TRUE SI ZB
69 0.817 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-175.5 -159.8 94.7 -189.5 62
Scaled residuals:
Min 1Q Median 3Q Max
-2.79058 -0.51699 0.02379 0.61577 1.65245
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001230 0.03507
PORT (Intercept) 0.001292 0.03594
Residual 0.002343 0.04840
Number of obs: 69, groups: DEST, 18; PORT, 14
Fixed effects:
Estimate Std. Error t value
(Intercept) 6.996e-01 2.816e-02 24.850
PRED_TRIPS -3.223e-05 1.484e-05 -2.172
PRED_ENV 3.351e-02 7.480e-03 4.480
ECO_DIFFTRUE -3.811e-03 2.031e-02 -0.188
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.328
PRED_ENV -0.540 0.209
ECO_DIFFTRU -0.623 0.247 -0.019
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 6.996488e-01 2.815538e-02 24.8495593
PRED_TRIPS -3.222894e-05 1.483686e-05 -2.1722204
PRED_ENV 3.350743e-02 7.480161e-03 4.4795066
ECO_DIFFTRUE -3.810566e-03 2.030875e-02 -0.1876317
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -173.39 -159.99 92.697 -185.39
full_model 7 -175.49 -159.85 94.743 -189.49 4.0929 1 0.04306 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "3" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 64 6 .# A tibble: 64 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.836 10 1.45 TRUE AD BT
2 0.924 6 1.96 TRUE AD HT
3 0.880 2 2.15 TRUE AD WL
4 0.708 15 1.06 TRUE AW WL
5 0.634 189 1.56 TRUE BT AW
6 0.708 20 1.50 TRUE BT GH
7 0.816 11 2.92 TRUE BT HN
8 0.800 287 1.52 FALSE BT HT
9 0.791 26 2.14 TRUE BT LB
10 0.751 75 3.16 FALSE BT MI
11 0.802 221 1.56 FALSE BT NO
12 0.702 6 1.50 TRUE BT OK
13 0.784 17 1.41 TRUE BT PL
14 0.688 5 1.49 TRUE BT RC
15 0.732 83 1.57 TRUE BT RT
16 0.740 180 0.921 FALSE BT WL
17 0.786 94 2.25 TRUE BT ZB
18 0.831 22 1.29 FALSE CB PL
19 0.688 11 0.547 FALSE CB RC
20 0.780 2 1.22 TRUE CB RT
21 0.671 11 1.07 TRUE GH WL
22 0.736 30 2.79 TRUE HN CB
23 0.830 30 2.81 TRUE HN HT
24 0.743 7 0.657 TRUE HN MI
25 0.781 316 2.11 TRUE HT AW
26 0.753 44 2.09 TRUE HT GH
27 0.892 93 2.53 TRUE HT LB
28 0.851 429 2.94 FALSE HT MI
29 0.635 3937 0.0459 FALSE HT NO
30 0.837 3 1.58 TRUE HT OK
31 0.654 21 1.88 TRUE HT PL
32 0.824 4 2.74 TRUE HT PM
33 0.845 37 1.88 TRUE HT RC
34 0.700 498 2.23 TRUE HT RT
35 0.644 31 1.55 FALSE HT WL
36 0.879 16 3.39 TRUE HT ZB
37 0.730 74 1.94 FALSE LB CB
38 0.726 11 1.50 TRUE LB MI
39 0.851 3 2.92 TRUE LB WL
40 0.747 114 4.15 TRUE MI AW
41 0.870 185 2.94 FALSE MI NO
42 0.715 8 3.38 TRUE MI OK
43 0.789 44 4.18 TRUE MI RT
44 0.802 2 3.49 TRUE MI ZB
45 0.653 11 1.59 FALSE NO WL
46 0.607 8 1.12 TRUE RT WL
47 0.815 556 2.06 TRUE SI AD
48 0.737 622 4.01 TRUE SI AW
49 0.747 142 3.18 TRUE SI BT
50 0.724 18 3.18 TRUE SI CB
51 0.790 77 3.93 TRUE SI GH
52 0.768 182 0.576 TRUE SI HN
53 0.837 435 2.81 TRUE SI HT
54 0.732 395 1.55 TRUE SI LB
55 0.703 24 0.506 TRUE SI MI
56 0.851 383 2.80 TRUE SI NO
57 0.698 126 3.17 TRUE SI OK
58 0.836 112 3.86 TRUE SI PL
59 0.780 13 2.54 TRUE SI PM
60 0.702 60 2.94 TRUE SI RC
61 0.774 1055 4.05 TRUE SI RT
62 0.801 12 3.84 TRUE SI WL
63 0.786 207 3.51 TRUE SI ZB
64 0.821 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-158.6 -143.5 86.3 -172.6 57
Scaled residuals:
Min 1Q Median 3Q Max
-2.48872 -0.60199 0.02656 0.63185 1.59683
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0009423 0.03070
PORT (Intercept) 0.0010344 0.03216
Residual 0.0027172 0.05213
Number of obs: 64, groups: DEST, 17; PORT, 13
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.106e-01 2.886e-02 24.618
PRED_TRIPS -3.083e-05 1.562e-05 -1.974
PRED_ENV 2.898e-02 8.178e-03 3.544
ECO_DIFFTRUE 2.099e-04 2.120e-02 0.010
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.344
PRED_ENV -0.579 0.207
ECO_DIFFTRU -0.595 0.246 -0.047
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.105561e-01 2.886310e-02 24.618149077
PRED_TRIPS -3.083354e-05 1.562214e-05 -1.973708071
PRED_ENV 2.898454e-02 8.177957e-03 3.544228017
ECO_DIFFTRUE 2.098470e-04 2.119563e-02 0.009900486
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -157.29 -144.34 84.645 -169.29
full_model 7 -158.63 -143.52 86.315 -172.63 3.3413 1 0.06756 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "4" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 69 6 .# A tibble: 69 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.836 10 1.45 TRUE AD BT
2 0.924 6 1.96 TRUE AD HT
3 0.880 2 2.15 TRUE AD WL
4 0.708 15 1.06 TRUE AW WL
5 0.634 189 1.56 TRUE BT AW
6 0.708 20 1.50 TRUE BT GH
7 0.816 11 2.92 TRUE BT HN
8 0.800 287 1.52 FALSE BT HT
9 0.791 26 2.14 TRUE BT LB
10 0.751 75 3.16 FALSE BT MI
11 0.802 221 1.56 FALSE BT NO
12 0.702 6 1.50 TRUE BT OK
13 0.784 17 1.41 TRUE BT PL
14 0.688 5 1.49 TRUE BT RC
15 0.732 83 1.57 TRUE BT RT
16 0.740 180 0.921 FALSE BT WL
17 0.786 94 2.25 TRUE BT ZB
18 0.831 22 1.29 FALSE CB PL
19 0.688 11 0.547 FALSE CB RC
20 0.780 2 1.22 TRUE CB RT
21 0.671 11 1.07 TRUE GH WL
22 0.736 30 2.79 TRUE HN CB
23 0.830 30 2.81 TRUE HN HT
24 0.743 7 0.657 TRUE HN MI
25 0.781 316 2.11 TRUE HT AW
26 0.753 44 2.09 TRUE HT GH
27 0.892 93 2.53 TRUE HT LB
28 0.851 429 2.94 FALSE HT MI
29 0.635 3937 0.0459 FALSE HT NO
30 0.837 3 1.58 TRUE HT OK
31 0.654 21 1.88 TRUE HT PL
32 0.824 4 2.74 TRUE HT PM
33 0.845 37 1.88 TRUE HT RC
34 0.700 498 2.23 TRUE HT RT
35 0.644 31 1.55 FALSE HT WL
36 0.879 16 3.39 TRUE HT ZB
37 0.730 74 1.94 FALSE LB CB
38 0.726 11 1.50 TRUE LB MI
39 0.851 3 2.92 TRUE LB WL
40 0.747 114 4.15 TRUE MI AW
41 0.870 185 2.94 FALSE MI NO
42 0.715 8 3.38 TRUE MI OK
43 0.789 44 4.18 TRUE MI RT
44 0.802 2 3.49 TRUE MI ZB
45 0.653 11 1.59 FALSE NO WL
46 0.771 0 2.92 TRUE PH BT
47 0.700 0 2.79 TRUE PH CB
48 0.863 0 2.81 TRUE PH HT
49 0.657 0 0.657 TRUE PH MI
50 0.714 0 0.576 TRUE PH SI
51 0.607 8 1.12 TRUE RT WL
52 0.815 556 2.06 TRUE SI AD
53 0.737 622 4.01 TRUE SI AW
54 0.747 142 3.18 TRUE SI BT
55 0.724 18 3.18 TRUE SI CB
56 0.790 77 3.93 TRUE SI GH
57 0.768 182 0.576 TRUE SI HN
58 0.837 435 2.81 TRUE SI HT
59 0.732 395 1.55 TRUE SI LB
60 0.703 24 0.506 TRUE SI MI
61 0.851 383 2.80 TRUE SI NO
62 0.698 126 3.17 TRUE SI OK
63 0.836 112 3.86 TRUE SI PL
64 0.780 13 2.54 TRUE SI PM
65 0.702 60 2.94 TRUE SI RC
66 0.774 1055 4.05 TRUE SI RT
67 0.801 12 3.84 TRUE SI WL
68 0.786 207 3.51 TRUE SI ZB
69 0.821 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-174.7 -159.0 94.3 -188.7 62
Scaled residuals:
Min 1Q Median 3Q Max
-2.59613 -0.55450 0.07882 0.59301 1.70025
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001116 0.03341
PORT (Intercept) 0.001042 0.03227
Residual 0.002506 0.05006
Number of obs: 69, groups: DEST, 18; PORT, 14
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.072e-01 2.788e-02 25.368
PRED_TRIPS -3.231e-05 1.516e-05 -2.131
PRED_ENV 3.053e-02 7.580e-03 4.028
ECO_DIFFTRUE -1.682e-03 2.051e-02 -0.082
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.333
PRED_ENV -0.553 0.203
ECO_DIFFTRU -0.626 0.246 -0.030
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.072436e-01 0.0278789750 25.36834883
PRED_TRIPS -3.230581e-05 0.0000151578 -2.13129866
PRED_ENV 3.053376e-02 0.0075801692 4.02811070
ECO_DIFFTRUE -1.681521e-03 0.0205080908 -0.08199305
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -172.74 -159.33 92.368 -184.74
full_model 7 -174.67 -159.03 94.334 -188.67 3.932 1 0.04738 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "5" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 64 6 .# A tibble: 64 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.983 10 1.45 TRUE AD BT
2 1 6 1.96 TRUE AD HT
3 0.996 2 2.15 TRUE AD WL
4 0.950 15 1.06 TRUE AW WL
5 0.902 189 1.56 TRUE BT AW
6 0.942 20 1.50 TRUE BT GH
7 0.985 11 2.92 TRUE BT HN
8 0.989 287 1.52 FALSE BT HT
9 0.964 26 2.14 TRUE BT LB
10 0.974 75 3.16 FALSE BT MI
11 0.985 221 1.56 FALSE BT NO
12 0.937 6 1.50 TRUE BT OK
13 0.991 17 1.41 TRUE BT PL
14 0.942 5 1.49 TRUE BT RC
15 0.955 83 1.57 TRUE BT RT
16 0.958 180 0.921 FALSE BT WL
17 0.955 94 2.25 TRUE BT ZB
18 1 22 1.29 FALSE CB PL
19 0.908 11 0.547 FALSE CB RC
20 0.989 2 1.22 TRUE CB RT
21 0.913 11 1.07 TRUE GH WL
22 0.980 30 2.79 TRUE HN CB
23 0.993 30 2.81 TRUE HN HT
24 0.940 7 0.657 TRUE HN MI
25 0.988 316 2.11 TRUE HT AW
26 0.969 44 2.09 TRUE HT GH
27 1.00 93 2.53 TRUE HT LB
28 0.994 429 2.94 FALSE HT MI
29 0.892 3937 0.0459 FALSE HT NO
30 0.998 3 1.58 TRUE HT OK
31 0.916 21 1.88 TRUE HT PL
32 0.997 4 2.74 TRUE HT PM
33 0.997 37 1.88 TRUE HT RC
34 0.948 498 2.23 TRUE HT RT
35 0.906 31 1.55 FALSE HT WL
36 0.999 16 3.39 TRUE HT ZB
37 0.940 74 1.94 FALSE LB CB
38 0.943 11 1.50 TRUE LB MI
39 0.997 3 2.92 TRUE LB WL
40 0.988 114 4.15 TRUE MI AW
41 0.998 185 2.94 FALSE MI NO
42 0.963 8 3.38 TRUE MI OK
43 0.991 44 4.18 TRUE MI RT
44 0.988 2 3.49 TRUE MI ZB
45 0.902 11 1.59 FALSE NO WL
46 0.894 8 1.12 TRUE RT WL
47 0.971 556 2.06 TRUE SI AD
48 0.985 622 4.01 TRUE SI AW
49 0.973 142 3.18 TRUE SI BT
50 0.981 18 3.18 TRUE SI CB
51 0.995 77 3.93 TRUE SI GH
52 0.959 182 0.576 TRUE SI HN
53 0.997 435 2.81 TRUE SI HT
54 0.967 395 1.55 TRUE SI LB
55 0.926 24 0.506 TRUE SI MI
56 0.997 383 2.80 TRUE SI NO
57 0.958 126 3.17 TRUE SI OK
58 0.998 112 3.86 TRUE SI PL
59 0.996 13 2.54 TRUE SI PM
60 0.965 60 2.94 TRUE SI RC
61 0.992 1055 4.05 TRUE SI RT
62 0.996 12 3.84 TRUE SI WL
63 0.984 207 3.51 TRUE SI ZB
64 0.996 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-278.9 -263.8 146.4 -292.9 57
Scaled residuals:
Min 1Q Median 3Q Max
-2.20269 -0.58867 -0.03706 0.75178 1.96334
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 2.44e-05 0.00494
PORT (Intercept) 0.00e+00 0.00000
Residual 5.80e-04 0.02408
Number of obs: 64, groups: DEST, 17; PORT, 13
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.293e-01 9.578e-03 97.025
PRED_TRIPS -8.721e-06 6.273e-06 -1.390
PRED_ENV 1.732e-02 3.115e-03 5.559
ECO_DIFFTRUE 2.657e-03 8.361e-03 0.318
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.354
PRED_ENV -0.586 0.106
ECO_DIFFTRU -0.574 0.217 -0.226
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 9.292775e-01 9.577734e-03 97.024780
PRED_TRIPS -8.721379e-06 6.272946e-06 -1.390316
PRED_ENV 1.731631e-02 3.115141e-03 5.558757
ECO_DIFFTRUE 2.657274e-03 8.361335e-03 0.317805
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -279.16 -266.20 145.58 -291.16
full_model 7 -278.86 -263.75 146.43 -292.86 1.7066 1 0.1914
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "6" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 69 6 .# A tibble: 69 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.983 10 1.45 TRUE AD BT
2 1 6 1.96 TRUE AD HT
3 0.996 2 2.15 TRUE AD WL
4 0.950 15 1.06 TRUE AW WL
5 0.902 189 1.56 TRUE BT AW
6 0.942 20 1.50 TRUE BT GH
7 0.985 11 2.92 TRUE BT HN
8 0.989 287 1.52 FALSE BT HT
9 0.964 26 2.14 TRUE BT LB
10 0.974 75 3.16 FALSE BT MI
11 0.985 221 1.56 FALSE BT NO
12 0.937 6 1.50 TRUE BT OK
13 0.991 17 1.41 TRUE BT PL
14 0.942 5 1.49 TRUE BT RC
15 0.955 83 1.57 TRUE BT RT
16 0.958 180 0.921 FALSE BT WL
17 0.955 94 2.25 TRUE BT ZB
18 1 22 1.29 FALSE CB PL
19 0.908 11 0.547 FALSE CB RC
20 0.989 2 1.22 TRUE CB RT
21 0.913 11 1.07 TRUE GH WL
22 0.980 30 2.79 TRUE HN CB
23 0.993 30 2.81 TRUE HN HT
24 0.940 7 0.657 TRUE HN MI
25 0.988 316 2.11 TRUE HT AW
26 0.969 44 2.09 TRUE HT GH
27 1.00 93 2.53 TRUE HT LB
28 0.994 429 2.94 FALSE HT MI
29 0.892 3937 0.0459 FALSE HT NO
30 0.998 3 1.58 TRUE HT OK
31 0.916 21 1.88 TRUE HT PL
32 0.997 4 2.74 TRUE HT PM
33 0.997 37 1.88 TRUE HT RC
34 0.948 498 2.23 TRUE HT RT
35 0.906 31 1.55 FALSE HT WL
36 0.999 16 3.39 TRUE HT ZB
37 0.940 74 1.94 FALSE LB CB
38 0.943 11 1.50 TRUE LB MI
39 0.997 3 2.92 TRUE LB WL
40 0.988 114 4.15 TRUE MI AW
41 0.998 185 2.94 FALSE MI NO
42 0.963 8 3.38 TRUE MI OK
43 0.991 44 4.18 TRUE MI RT
44 0.988 2 3.49 TRUE MI ZB
45 0.902 11 1.59 FALSE NO WL
46 0.974 0 2.92 TRUE PH BT
47 0.973 0 2.79 TRUE PH CB
48 0.999 0 2.81 TRUE PH HT
49 0.910 0 0.657 TRUE PH MI
50 0.931 0 0.576 TRUE PH SI
51 0.894 8 1.12 TRUE RT WL
52 0.971 556 2.06 TRUE SI AD
53 0.985 622 4.01 TRUE SI AW
54 0.973 142 3.18 TRUE SI BT
55 0.981 18 3.18 TRUE SI CB
56 0.995 77 3.93 TRUE SI GH
57 0.959 182 0.576 TRUE SI HN
58 0.997 435 2.81 TRUE SI HT
59 0.967 395 1.55 TRUE SI LB
60 0.926 24 0.506 TRUE SI MI
61 0.997 383 2.80 TRUE SI NO
62 0.958 126 3.17 TRUE SI OK
63 0.998 112 3.86 TRUE SI PL
64 0.996 13 2.54 TRUE SI PM
65 0.965 60 2.94 TRUE SI RC
66 0.992 1055 4.05 TRUE SI RT
67 0.996 12 3.84 TRUE SI WL
68 0.984 207 3.51 TRUE SI ZB
69 0.996 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-304.5 -288.9 159.3 -318.5 62
Scaled residuals:
Min 1Q Median 3Q Max
-2.16269 -0.57072 -0.08068 0.74398 2.00855
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 3.202e-05 0.005659
PORT (Intercept) 0.000e+00 0.000000
Residual 5.499e-04 0.023450
Number of obs: 69, groups: DEST, 18; PORT, 14
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.277e-01 9.270e-03 100.076
PRED_TRIPS -8.556e-06 6.130e-06 -1.396
PRED_ENV 1.830e-02 2.897e-03 6.318
ECO_DIFFTRUE 1.228e-03 8.085e-03 0.152
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.349
PRED_ENV -0.566 0.098
ECO_DIFFTRU -0.615 0.229 -0.197
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 9.276890e-01 9.269852e-03 100.0759274
PRED_TRIPS -8.555574e-06 6.129774e-06 -1.3957405
PRED_ENV 1.830313e-02 2.897061e-03 6.3178267
ECO_DIFFTRUE 1.228085e-03 8.085449e-03 0.1518882
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -304.77 -291.37 158.39 -316.77
full_model 7 -304.53 -288.89 159.26 -318.53 1.753 1 0.1855
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "7" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 64 6 .# A tibble: 64 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.955 10 1.45 TRUE AD BT
2 0.999 6 1.96 TRUE AD HT
3 0.984 2 2.15 TRUE AD WL
4 0.919 15 1.06 TRUE AW WL
5 0.848 189 1.56 TRUE BT AW
6 0.907 20 1.50 TRUE BT GH
7 0.973 11 2.92 TRUE BT HN
8 0.980 287 1.52 FALSE BT HT
9 0.938 26 2.14 TRUE BT LB
10 0.934 75 3.16 FALSE BT MI
11 0.971 221 1.56 FALSE BT NO
12 0.888 6 1.50 TRUE BT OK
13 0.982 17 1.41 TRUE BT PL
14 0.904 5 1.49 TRUE BT RC
15 0.928 83 1.57 TRUE BT RT
16 0.935 180 0.921 FALSE BT WL
17 0.932 94 2.25 TRUE BT ZB
18 0.998 22 1.29 FALSE CB PL
19 0.863 11 0.547 FALSE CB RC
20 0.970 2 1.22 TRUE CB RT
21 0.879 11 1.07 TRUE GH WL
22 0.948 30 2.79 TRUE HN CB
23 0.988 30 2.81 TRUE HN HT
24 0.902 7 0.657 TRUE HN MI
25 0.981 316 2.11 TRUE HT AW
26 0.951 44 2.09 TRUE HT GH
27 0.998 93 2.53 TRUE HT LB
28 0.987 429 2.94 FALSE HT MI
29 0.848 3937 0.0459 FALSE HT NO
30 0.990 3 1.58 TRUE HT OK
31 0.867 21 1.88 TRUE HT PL
32 0.995 4 2.74 TRUE HT PM
33 0.992 37 1.88 TRUE HT RC
34 0.921 498 2.23 TRUE HT RT
35 0.866 31 1.55 FALSE HT WL
36 0.997 16 3.39 TRUE HT ZB
37 0.902 74 1.94 FALSE LB CB
38 0.908 11 1.50 TRUE LB MI
39 0.991 3 2.92 TRUE LB WL
40 0.967 114 4.15 TRUE MI AW
41 0.990 185 2.94 FALSE MI NO
42 0.928 8 3.38 TRUE MI OK
43 0.977 44 4.18 TRUE MI RT
44 0.966 2 3.49 TRUE MI ZB
45 0.857 11 1.59 FALSE NO WL
46 0.840 8 1.12 TRUE RT WL
47 0.946 556 2.06 TRUE SI AD
48 0.960 622 4.01 TRUE SI AW
49 0.947 142 3.18 TRUE SI BT
50 0.950 18 3.18 TRUE SI CB
51 0.986 77 3.93 TRUE SI GH
52 0.935 182 0.576 TRUE SI HN
53 0.993 435 2.81 TRUE SI HT
54 0.932 395 1.55 TRUE SI LB
55 0.883 24 0.506 TRUE SI MI
56 0.994 383 2.80 TRUE SI NO
57 0.922 126 3.17 TRUE SI OK
58 0.996 112 3.86 TRUE SI PL
59 0.983 13 2.54 TRUE SI PM
60 0.930 60 2.94 TRUE SI RC
61 0.976 1055 4.05 TRUE SI RT
62 0.986 12 3.84 TRUE SI WL
63 0.952 207 3.51 TRUE SI ZB
64 0.987 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-225.8 -210.7 119.9 -239.8 57
Scaled residuals:
Min 1Q Median 3Q Max
-2.1578 -0.6235 -0.0341 0.7580 1.9540
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 7.188e-05 0.008478
PORT (Intercept) 0.000e+00 0.000000
Residual 1.316e-03 0.036276
Number of obs: 64, groups: DEST, 17; PORT, 13
Fixed effects:
Estimate Std. Error t value
(Intercept) 8.945e-01 1.456e-02 61.445
PRED_TRIPS -1.115e-05 9.499e-06 -1.174
PRED_ENV 2.318e-02 4.707e-03 4.925
ECO_DIFFTRUE 1.773e-03 1.264e-02 0.140
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.353
PRED_ENV -0.586 0.109
ECO_DIFFTRU -0.575 0.214 -0.221
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 8.944901e-01 1.455752e-02 61.4452131
PRED_TRIPS -1.115435e-05 9.498993e-06 -1.1742663
PRED_ENV 2.317944e-02 4.706715e-03 4.9247586
ECO_DIFFTRUE 1.773206e-03 1.264349e-02 0.1402466
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -226.56 -213.61 119.28 -238.56
full_model 7 -225.77 -210.66 119.89 -239.77 1.2075 1 0.2718
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "8" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 69 6 .# A tibble: 69 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.955 10 1.45 TRUE AD BT
2 0.999 6 1.96 TRUE AD HT
3 0.984 2 2.15 TRUE AD WL
4 0.919 15 1.06 TRUE AW WL
5 0.848 189 1.56 TRUE BT AW
6 0.907 20 1.50 TRUE BT GH
7 0.973 11 2.92 TRUE BT HN
8 0.980 287 1.52 FALSE BT HT
9 0.938 26 2.14 TRUE BT LB
10 0.934 75 3.16 FALSE BT MI
11 0.971 221 1.56 FALSE BT NO
12 0.888 6 1.50 TRUE BT OK
13 0.982 17 1.41 TRUE BT PL
14 0.904 5 1.49 TRUE BT RC
15 0.928 83 1.57 TRUE BT RT
16 0.935 180 0.921 FALSE BT WL
17 0.932 94 2.25 TRUE BT ZB
18 0.998 22 1.29 FALSE CB PL
19 0.863 11 0.547 FALSE CB RC
20 0.970 2 1.22 TRUE CB RT
21 0.879 11 1.07 TRUE GH WL
22 0.948 30 2.79 TRUE HN CB
23 0.988 30 2.81 TRUE HN HT
24 0.902 7 0.657 TRUE HN MI
25 0.981 316 2.11 TRUE HT AW
26 0.951 44 2.09 TRUE HT GH
27 0.998 93 2.53 TRUE HT LB
28 0.987 429 2.94 FALSE HT MI
29 0.848 3937 0.0459 FALSE HT NO
30 0.990 3 1.58 TRUE HT OK
31 0.867 21 1.88 TRUE HT PL
32 0.995 4 2.74 TRUE HT PM
33 0.992 37 1.88 TRUE HT RC
34 0.921 498 2.23 TRUE HT RT
35 0.866 31 1.55 FALSE HT WL
36 0.997 16 3.39 TRUE HT ZB
37 0.902 74 1.94 FALSE LB CB
38 0.908 11 1.50 TRUE LB MI
39 0.991 3 2.92 TRUE LB WL
40 0.967 114 4.15 TRUE MI AW
41 0.990 185 2.94 FALSE MI NO
42 0.928 8 3.38 TRUE MI OK
43 0.977 44 4.18 TRUE MI RT
44 0.966 2 3.49 TRUE MI ZB
45 0.857 11 1.59 FALSE NO WL
46 0.946 0 2.92 TRUE PH BT
47 0.931 0 2.79 TRUE PH CB
48 0.997 0 2.81 TRUE PH HT
49 0.844 0 0.657 TRUE PH MI
50 0.898 0 0.576 TRUE PH SI
51 0.840 8 1.12 TRUE RT WL
52 0.946 556 2.06 TRUE SI AD
53 0.960 622 4.01 TRUE SI AW
54 0.947 142 3.18 TRUE SI BT
55 0.950 18 3.18 TRUE SI CB
56 0.986 77 3.93 TRUE SI GH
57 0.935 182 0.576 TRUE SI HN
58 0.993 435 2.81 TRUE SI HT
59 0.932 395 1.55 TRUE SI LB
60 0.883 24 0.506 TRUE SI MI
61 0.994 383 2.80 TRUE SI NO
62 0.922 126 3.17 TRUE SI OK
63 0.996 112 3.86 TRUE SI PL
64 0.983 13 2.54 TRUE SI PM
65 0.930 60 2.94 TRUE SI RC
66 0.976 1055 4.05 TRUE SI RT
67 0.986 12 3.84 TRUE SI WL
68 0.952 207 3.51 TRUE SI ZB
69 0.987 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-245.5 -229.8 129.7 -259.5 62
Scaled residuals:
Min 1Q Median 3Q Max
-2.14268 -0.57961 -0.05054 0.80019 1.94776
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001275 0.01129
PORT (Intercept) 0.0000000 0.00000
Residual 0.0012530 0.03540
Number of obs: 69, groups: DEST, 18; PORT, 14
Fixed effects:
Estimate Std. Error t value
(Intercept) 8.924e-01 1.437e-02 62.097
PRED_TRIPS -1.142e-05 9.396e-06 -1.215
PRED_ENV 2.470e-02 4.420e-03 5.588
ECO_DIFFTRUE -9.740e-04 1.234e-02 -0.079
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.345
PRED_ENV -0.567 0.106
ECO_DIFFTRU -0.616 0.221 -0.185
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 8.924022e-01 1.437121e-02 62.09653937
PRED_TRIPS -1.141679e-05 9.395606e-06 -1.21512014
PRED_ENV 2.470216e-02 4.420316e-03 5.58832543
ECO_DIFFTRUE -9.739660e-04 1.233857e-02 -0.07893667
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -246.15 -232.74 129.07 -258.15
full_model 7 -245.49 -229.85 129.74 -259.49 1.3392 1 0.2472
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "9" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 64 6 .# A tibble: 64 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.815 10 1.45 TRUE AD BT
2 0.925 6 1.96 TRUE AD HT
3 0.881 2 2.15 TRUE AD WL
4 0.702 15 1.06 TRUE AW WL
5 0.634 189 1.56 TRUE BT AW
6 0.713 20 1.50 TRUE BT GH
7 0.819 11 2.92 TRUE BT HN
8 0.800 287 1.52 FALSE BT HT
9 0.782 26 2.14 TRUE BT LB
10 0.760 75 3.16 FALSE BT MI
11 0.801 221 1.56 FALSE BT NO
12 0.693 6 1.50 TRUE BT OK
13 0.778 17 1.41 TRUE BT PL
14 0.683 5 1.49 TRUE BT RC
15 0.728 83 1.57 TRUE BT RT
16 0.728 180 0.921 FALSE BT WL
17 0.764 94 2.25 TRUE BT ZB
18 0.836 22 1.29 FALSE CB PL
19 0.683 11 0.547 FALSE CB RC
20 0.779 2 1.22 TRUE CB RT
21 0.673 11 1.07 TRUE GH WL
22 0.741 30 2.79 TRUE HN CB
23 0.840 30 2.81 TRUE HN HT
24 0.736 7 0.657 TRUE HN MI
25 0.779 316 2.11 TRUE HT AW
26 0.765 44 2.09 TRUE HT GH
27 0.889 93 2.53 TRUE HT LB
28 0.849 429 2.94 FALSE HT MI
29 0.632 3937 0.0459 FALSE HT NO
30 0.829 3 1.58 TRUE HT OK
31 0.645 21 1.88 TRUE HT PL
32 0.838 4 2.74 TRUE HT PM
33 0.839 37 1.88 TRUE HT RC
34 0.701 498 2.23 TRUE HT RT
35 0.653 31 1.55 FALSE HT WL
36 0.874 16 3.39 TRUE HT ZB
37 0.735 74 1.94 FALSE LB CB
38 0.721 11 1.50 TRUE LB MI
39 0.846 3 2.92 TRUE LB WL
40 0.745 114 4.15 TRUE MI AW
41 0.869 185 2.94 FALSE MI NO
42 0.719 8 3.38 TRUE MI OK
43 0.785 44 4.18 TRUE MI RT
44 0.790 2 3.49 TRUE MI ZB
45 0.658 11 1.59 FALSE NO WL
46 0.603 8 1.12 TRUE RT WL
47 0.796 556 2.06 TRUE SI AD
48 0.741 622 4.01 TRUE SI AW
49 0.743 142 3.18 TRUE SI BT
50 0.737 18 3.18 TRUE SI CB
51 0.797 77 3.93 TRUE SI GH
52 0.761 182 0.576 TRUE SI HN
53 0.842 435 2.81 TRUE SI HT
54 0.727 395 1.55 TRUE SI LB
55 0.692 24 0.506 TRUE SI MI
56 0.858 383 2.80 TRUE SI NO
57 0.706 126 3.17 TRUE SI OK
58 0.841 112 3.86 TRUE SI PL
59 0.781 13 2.54 TRUE SI PM
60 0.699 60 2.94 TRUE SI RC
61 0.777 1055 4.05 TRUE SI RT
62 0.803 12 3.84 TRUE SI WL
63 0.777 207 3.51 TRUE SI ZB
64 0.813 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-161.4 -146.2 87.7 -175.4 57
Scaled residuals:
Min 1Q Median 3Q Max
-2.73057 -0.51951 0.01372 0.66037 1.59054
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0009642 0.03105
PORT (Intercept) 0.0011367 0.03371
Residual 0.0025268 0.05027
Number of obs: 64, groups: DEST, 17; PORT, 13
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.043e-01 2.853e-02 24.682
PRED_TRIPS -3.207e-05 1.518e-05 -2.113
PRED_ENV 3.265e-02 7.982e-03 4.090
ECO_DIFFTRUE -3.711e-03 2.071e-02 -0.179
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.341
PRED_ENV -0.573 0.212
ECO_DIFFTRU -0.595 0.247 -0.039
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.042866e-01 2.853415e-02 24.6822345
PRED_TRIPS -3.207035e-05 1.517845e-05 -2.1128872
PRED_ENV 3.264965e-02 7.982089e-03 4.0903635
ECO_DIFFTRUE -3.711237e-03 2.070999e-02 -0.1792004
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -159.56 -146.60 85.778 -171.56
full_model 7 -161.36 -146.25 87.679 -175.36 3.8022 1 0.05119 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "10" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 69 6 .# A tibble: 69 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.815 10 1.45 TRUE AD BT
2 0.925 6 1.96 TRUE AD HT
3 0.881 2 2.15 TRUE AD WL
4 0.702 15 1.06 TRUE AW WL
5 0.634 189 1.56 TRUE BT AW
6 0.713 20 1.50 TRUE BT GH
7 0.819 11 2.92 TRUE BT HN
8 0.800 287 1.52 FALSE BT HT
9 0.782 26 2.14 TRUE BT LB
10 0.760 75 3.16 FALSE BT MI
11 0.801 221 1.56 FALSE BT NO
12 0.693 6 1.50 TRUE BT OK
13 0.778 17 1.41 TRUE BT PL
14 0.683 5 1.49 TRUE BT RC
15 0.728 83 1.57 TRUE BT RT
16 0.728 180 0.921 FALSE BT WL
17 0.764 94 2.25 TRUE BT ZB
18 0.836 22 1.29 FALSE CB PL
19 0.683 11 0.547 FALSE CB RC
20 0.779 2 1.22 TRUE CB RT
21 0.673 11 1.07 TRUE GH WL
22 0.741 30 2.79 TRUE HN CB
23 0.840 30 2.81 TRUE HN HT
24 0.736 7 0.657 TRUE HN MI
25 0.779 316 2.11 TRUE HT AW
26 0.765 44 2.09 TRUE HT GH
27 0.889 93 2.53 TRUE HT LB
28 0.849 429 2.94 FALSE HT MI
29 0.632 3937 0.0459 FALSE HT NO
30 0.829 3 1.58 TRUE HT OK
31 0.645 21 1.88 TRUE HT PL
32 0.838 4 2.74 TRUE HT PM
33 0.839 37 1.88 TRUE HT RC
34 0.701 498 2.23 TRUE HT RT
35 0.653 31 1.55 FALSE HT WL
36 0.874 16 3.39 TRUE HT ZB
37 0.735 74 1.94 FALSE LB CB
38 0.721 11 1.50 TRUE LB MI
39 0.846 3 2.92 TRUE LB WL
40 0.745 114 4.15 TRUE MI AW
41 0.869 185 2.94 FALSE MI NO
42 0.719 8 3.38 TRUE MI OK
43 0.785 44 4.18 TRUE MI RT
44 0.790 2 3.49 TRUE MI ZB
45 0.658 11 1.59 FALSE NO WL
46 0.779 0 2.92 TRUE PH BT
47 0.708 0 2.79 TRUE PH CB
48 0.872 0 2.81 TRUE PH HT
49 0.657 0 0.657 TRUE PH MI
50 0.701 0 0.576 TRUE PH SI
51 0.603 8 1.12 TRUE RT WL
52 0.796 556 2.06 TRUE SI AD
53 0.741 622 4.01 TRUE SI AW
54 0.743 142 3.18 TRUE SI BT
55 0.737 18 3.18 TRUE SI CB
56 0.797 77 3.93 TRUE SI GH
57 0.761 182 0.576 TRUE SI HN
58 0.842 435 2.81 TRUE SI HT
59 0.727 395 1.55 TRUE SI LB
60 0.692 24 0.506 TRUE SI MI
61 0.858 383 2.80 TRUE SI NO
62 0.706 126 3.17 TRUE SI OK
63 0.841 112 3.86 TRUE SI PL
64 0.781 13 2.54 TRUE SI PM
65 0.699 60 2.94 TRUE SI RC
66 0.777 1055 4.05 TRUE SI RT
67 0.803 12 3.84 TRUE SI WL
68 0.777 207 3.51 TRUE SI ZB
69 0.813 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0072036 (tol = 0.002, component 1)
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-177.8 -162.2 95.9 -191.8 62
Scaled residuals:
Min 1Q Median 3Q Max
-2.84056 -0.49506 -0.01335 0.63641 1.68332
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001126 0.03355
PORT (Intercept) 0.001092 0.03304
Residual 0.002338 0.04836
Number of obs: 69, groups: DEST, 18; PORT, 14
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.006e-01 2.743e-02 25.538
PRED_TRIPS -3.332e-05 1.472e-05 -2.263
PRED_ENV 3.438e-02 7.387e-03 4.654
ECO_DIFFTRUE -5.084e-03 2.001e-02 -0.254
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.331
PRED_ENV -0.548 0.205
ECO_DIFFTRU -0.625 0.246 -0.025
convergence code: 0
Model failed to converge with max|grad| = 0.0072036 (tol = 0.002, component 1)
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.005728e-01 2.743285e-02 25.5377325
PRED_TRIPS -3.331905e-05 1.472425e-05 -2.2628692
PRED_ENV 3.438061e-02 7.387069e-03 4.6541618
ECO_DIFFTRUE -5.084094e-03 2.001283e-02 -0.2540418
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -175.38 -161.97 93.687 -187.38
full_model 7 -177.80 -162.16 95.899 -191.80 4.4231 1 0.03546 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0072036 (tol = 0.002, component 1)
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "11" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 64 6 .# A tibble: 64 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.826 10 1.45 TRUE AD BT
2 0.927 6 1.96 TRUE AD HT
3 0.884 2 2.15 TRUE AD WL
4 0.716 15 1.06 TRUE AW WL
5 0.625 189 1.56 TRUE BT AW
6 0.705 20 1.50 TRUE BT GH
7 0.811 11 2.92 TRUE BT HN
8 0.800 287 1.52 FALSE BT HT
9 0.777 26 2.14 TRUE BT LB
10 0.750 75 3.16 FALSE BT MI
11 0.795 221 1.56 FALSE BT NO
12 0.695 6 1.50 TRUE BT OK
13 0.783 17 1.41 TRUE BT PL
14 0.678 5 1.49 TRUE BT RC
15 0.735 83 1.57 TRUE BT RT
16 0.733 180 0.921 FALSE BT WL
17 0.776 94 2.25 TRUE BT ZB
18 0.840 22 1.29 FALSE CB PL
19 0.675 11 0.547 FALSE CB RC
20 0.780 2 1.22 TRUE CB RT
21 0.675 11 1.07 TRUE GH WL
22 0.731 30 2.79 TRUE HN CB
23 0.836 30 2.81 TRUE HN HT
24 0.729 7 0.657 TRUE HN MI
25 0.781 316 2.11 TRUE HT AW
26 0.757 44 2.09 TRUE HT GH
27 0.891 93 2.53 TRUE HT LB
28 0.852 429 2.94 FALSE HT MI
29 0.619 3937 0.0459 FALSE HT NO
30 0.836 3 1.58 TRUE HT OK
31 0.636 21 1.88 TRUE HT PL
32 0.830 4 2.74 TRUE HT PM
33 0.839 37 1.88 TRUE HT RC
34 0.694 498 2.23 TRUE HT RT
35 0.642 31 1.55 FALSE HT WL
36 0.878 16 3.39 TRUE HT ZB
37 0.724 74 1.94 FALSE LB CB
38 0.710 11 1.50 TRUE LB MI
39 0.847 3 2.92 TRUE LB WL
40 0.745 114 4.15 TRUE MI AW
41 0.870 185 2.94 FALSE MI NO
42 0.706 8 3.38 TRUE MI OK
43 0.793 44 4.18 TRUE MI RT
44 0.801 2 3.49 TRUE MI ZB
45 0.645 11 1.59 FALSE NO WL
46 0.607 8 1.12 TRUE RT WL
47 0.805 556 2.06 TRUE SI AD
48 0.734 622 4.01 TRUE SI AW
49 0.741 142 3.18 TRUE SI BT
50 0.736 18 3.18 TRUE SI CB
51 0.800 77 3.93 TRUE SI GH
52 0.765 182 0.576 TRUE SI HN
53 0.843 435 2.81 TRUE SI HT
54 0.721 395 1.55 TRUE SI LB
55 0.699 24 0.506 TRUE SI MI
56 0.857 383 2.80 TRUE SI NO
57 0.706 126 3.17 TRUE SI OK
58 0.843 112 3.86 TRUE SI PL
59 0.784 13 2.54 TRUE SI PM
60 0.692 60 2.94 TRUE SI RC
61 0.777 1055 4.05 TRUE SI RT
62 0.806 12 3.84 TRUE SI WL
63 0.777 207 3.51 TRUE SI ZB
64 0.830 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-153.9 -138.8 84.0 -167.9 57
Scaled residuals:
Min 1Q Median 3Q Max
-2.6048 -0.5659 0.0599 0.6676 1.5485
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0008742 0.02957
PORT (Intercept) 0.0009123 0.03020
Residual 0.0030711 0.05542
Number of obs: 64, groups: DEST, 17; PORT, 13
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.054e-01 2.950e-02 23.916
PRED_TRIPS -3.267e-05 1.638e-05 -1.994
PRED_ENV 3.089e-02 8.526e-03 3.623
ECO_DIFFTRUE -1.012e-03 2.206e-02 -0.046
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.348
PRED_ENV -0.588 0.200
ECO_DIFFTRU -0.594 0.245 -0.062
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.054095e-01 2.949584e-02 23.91556221
PRED_TRIPS -3.266542e-05 1.637927e-05 -1.99431443
PRED_ENV 3.089044e-02 8.526501e-03 3.62287454
ECO_DIFFTRUE -1.011739e-03 2.206397e-02 -0.04585479
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -152.53 -139.58 82.266 -164.53
full_model 7 -153.91 -138.80 83.956 -167.91 3.3786 1 0.06605 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "12" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 69 6 .# A tibble: 69 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.826 10 1.45 TRUE AD BT
2 0.927 6 1.96 TRUE AD HT
3 0.884 2 2.15 TRUE AD WL
4 0.716 15 1.06 TRUE AW WL
5 0.625 189 1.56 TRUE BT AW
6 0.705 20 1.50 TRUE BT GH
7 0.811 11 2.92 TRUE BT HN
8 0.800 287 1.52 FALSE BT HT
9 0.777 26 2.14 TRUE BT LB
10 0.750 75 3.16 FALSE BT MI
11 0.795 221 1.56 FALSE BT NO
12 0.695 6 1.50 TRUE BT OK
13 0.783 17 1.41 TRUE BT PL
14 0.678 5 1.49 TRUE BT RC
15 0.735 83 1.57 TRUE BT RT
16 0.733 180 0.921 FALSE BT WL
17 0.776 94 2.25 TRUE BT ZB
18 0.840 22 1.29 FALSE CB PL
19 0.675 11 0.547 FALSE CB RC
20 0.780 2 1.22 TRUE CB RT
21 0.675 11 1.07 TRUE GH WL
22 0.731 30 2.79 TRUE HN CB
23 0.836 30 2.81 TRUE HN HT
24 0.729 7 0.657 TRUE HN MI
25 0.781 316 2.11 TRUE HT AW
26 0.757 44 2.09 TRUE HT GH
27 0.891 93 2.53 TRUE HT LB
28 0.852 429 2.94 FALSE HT MI
29 0.619 3937 0.0459 FALSE HT NO
30 0.836 3 1.58 TRUE HT OK
31 0.636 21 1.88 TRUE HT PL
32 0.830 4 2.74 TRUE HT PM
33 0.839 37 1.88 TRUE HT RC
34 0.694 498 2.23 TRUE HT RT
35 0.642 31 1.55 FALSE HT WL
36 0.878 16 3.39 TRUE HT ZB
37 0.724 74 1.94 FALSE LB CB
38 0.710 11 1.50 TRUE LB MI
39 0.847 3 2.92 TRUE LB WL
40 0.745 114 4.15 TRUE MI AW
41 0.870 185 2.94 FALSE MI NO
42 0.706 8 3.38 TRUE MI OK
43 0.793 44 4.18 TRUE MI RT
44 0.801 2 3.49 TRUE MI ZB
45 0.645 11 1.59 FALSE NO WL
46 0.765 0 2.92 TRUE PH BT
47 0.698 0 2.79 TRUE PH CB
48 0.871 0 2.81 TRUE PH HT
49 0.643 0 0.657 TRUE PH MI
50 0.703 0 0.576 TRUE PH SI
51 0.607 8 1.12 TRUE RT WL
52 0.805 556 2.06 TRUE SI AD
53 0.734 622 4.01 TRUE SI AW
54 0.741 142 3.18 TRUE SI BT
55 0.736 18 3.18 TRUE SI CB
56 0.800 77 3.93 TRUE SI GH
57 0.765 182 0.576 TRUE SI HN
58 0.843 435 2.81 TRUE SI HT
59 0.721 395 1.55 TRUE SI LB
60 0.699 24 0.506 TRUE SI MI
61 0.857 383 2.80 TRUE SI NO
62 0.706 126 3.17 TRUE SI OK
63 0.843 112 3.86 TRUE SI PL
64 0.784 13 2.54 TRUE SI PM
65 0.692 60 2.94 TRUE SI RC
66 0.777 1055 4.05 TRUE SI RT
67 0.806 12 3.84 TRUE SI WL
68 0.777 207 3.51 TRUE SI ZB
69 0.830 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-169.2 -153.5 91.6 -183.2 62
Scaled residuals:
Min 1Q Median 3Q Max
-2.72407 -0.56034 0.04259 0.61408 1.65843
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001087 0.03297
PORT (Intercept) 0.000962 0.03102
Residual 0.002826 0.05316
Number of obs: 69, groups: DEST, 18; PORT, 14
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.011e-01 2.868e-02 24.444
PRED_TRIPS -3.432e-05 1.593e-05 -2.154
PRED_ENV 3.281e-02 7.924e-03 4.140
ECO_DIFFTRUE -3.041e-03 2.140e-02 -0.142
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.337
PRED_ENV -0.561 0.197
ECO_DIFFTRU -0.627 0.245 -0.040
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.011007e-01 2.868200e-02 24.4439243
PRED_TRIPS -3.431843e-05 1.592986e-05 -2.1543462
PRED_ENV 3.280768e-02 7.924264e-03 4.1401548
ECO_DIFFTRUE -3.041145e-03 2.140271e-02 -0.1420916
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -167.19 -153.79 89.597 -179.19
full_model 7 -169.19 -153.55 91.594 -183.19 3.9944 1 0.04565 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "13" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 64 6 .# A tibble: 64 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.983 10 1.45 TRUE AD BT
2 1 6 1.96 TRUE AD HT
3 0.996 2 2.15 TRUE AD WL
4 0.951 15 1.06 TRUE AW WL
5 0.902 189 1.56 TRUE BT AW
6 0.943 20 1.50 TRUE BT GH
7 0.985 11 2.92 TRUE BT HN
8 0.989 287 1.52 FALSE BT HT
9 0.964 26 2.14 TRUE BT LB
10 0.974 75 3.16 FALSE BT MI
11 0.985 221 1.56 FALSE BT NO
12 0.936 6 1.50 TRUE BT OK
13 0.991 17 1.41 TRUE BT PL
14 0.941 5 1.49 TRUE BT RC
15 0.955 83 1.57 TRUE BT RT
16 0.957 180 0.921 FALSE BT WL
17 0.956 94 2.25 TRUE BT ZB
18 1 22 1.29 FALSE CB PL
19 0.907 11 0.547 FALSE CB RC
20 0.989 2 1.22 TRUE CB RT
21 0.913 11 1.07 TRUE GH WL
22 0.980 30 2.79 TRUE HN CB
23 0.993 30 2.81 TRUE HN HT
24 0.938 7 0.657 TRUE HN MI
25 0.988 316 2.11 TRUE HT AW
26 0.969 44 2.09 TRUE HT GH
27 1.00 93 2.53 TRUE HT LB
28 0.994 429 2.94 FALSE HT MI
29 0.891 3937 0.0459 FALSE HT NO
30 0.998 3 1.58 TRUE HT OK
31 0.915 21 1.88 TRUE HT PL
32 0.997 4 2.74 TRUE HT PM
33 0.997 37 1.88 TRUE HT RC
34 0.948 498 2.23 TRUE HT RT
35 0.905 31 1.55 FALSE HT WL
36 0.998 16 3.39 TRUE HT ZB
37 0.939 74 1.94 FALSE LB CB
38 0.942 11 1.50 TRUE LB MI
39 0.997 3 2.92 TRUE LB WL
40 0.988 114 4.15 TRUE MI AW
41 0.998 185 2.94 FALSE MI NO
42 0.962 8 3.38 TRUE MI OK
43 0.991 44 4.18 TRUE MI RT
44 0.988 2 3.49 TRUE MI ZB
45 0.902 11 1.59 FALSE NO WL
46 0.894 8 1.12 TRUE RT WL
47 0.971 556 2.06 TRUE SI AD
48 0.986 622 4.01 TRUE SI AW
49 0.973 142 3.18 TRUE SI BT
50 0.982 18 3.18 TRUE SI CB
51 0.996 77 3.93 TRUE SI GH
52 0.958 182 0.576 TRUE SI HN
53 0.997 435 2.81 TRUE SI HT
54 0.966 395 1.55 TRUE SI LB
55 0.926 24 0.506 TRUE SI MI
56 0.998 383 2.80 TRUE SI NO
57 0.959 126 3.17 TRUE SI OK
58 0.998 112 3.86 TRUE SI PL
59 0.996 13 2.54 TRUE SI PM
60 0.967 60 2.94 TRUE SI RC
61 0.993 1055 4.05 TRUE SI RT
62 0.997 12 3.84 TRUE SI WL
63 0.986 207 3.51 TRUE SI ZB
64 0.996 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-278.6 -263.5 146.3 -292.6 57
Scaled residuals:
Min 1Q Median 3Q Max
-2.20197 -0.58910 -0.03792 0.75457 1.98232
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 2.311e-05 0.004807
PORT (Intercept) 0.000e+00 0.000000
Residual 5.840e-04 0.024165
Number of obs: 64, groups: DEST, 17; PORT, 13
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.283e-01 9.592e-03 96.779
PRED_TRIPS -8.624e-06 6.287e-06 -1.372
PRED_ENV 1.766e-02 3.124e-03 5.653
ECO_DIFFTRUE 2.875e-03 8.383e-03 0.343
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.354
PRED_ENV -0.586 0.106
ECO_DIFFTRU -0.574 0.217 -0.227
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 9.282843e-01 9.591843e-03 96.7785153
PRED_TRIPS -8.624270e-06 6.287087e-06 -1.3717433
PRED_ENV 1.765906e-02 3.123606e-03 5.6534209
ECO_DIFFTRUE 2.874895e-03 8.382687e-03 0.3429563
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -278.90 -265.95 145.45 -290.90
full_model 7 -278.57 -263.45 146.28 -292.57 1.6621 1 0.1973
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "14" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 69 6 .# A tibble: 69 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.983 10 1.45 TRUE AD BT
2 1 6 1.96 TRUE AD HT
3 0.996 2 2.15 TRUE AD WL
4 0.951 15 1.06 TRUE AW WL
5 0.902 189 1.56 TRUE BT AW
6 0.943 20 1.50 TRUE BT GH
7 0.985 11 2.92 TRUE BT HN
8 0.989 287 1.52 FALSE BT HT
9 0.964 26 2.14 TRUE BT LB
10 0.974 75 3.16 FALSE BT MI
11 0.985 221 1.56 FALSE BT NO
12 0.936 6 1.50 TRUE BT OK
13 0.991 17 1.41 TRUE BT PL
14 0.941 5 1.49 TRUE BT RC
15 0.955 83 1.57 TRUE BT RT
16 0.957 180 0.921 FALSE BT WL
17 0.956 94 2.25 TRUE BT ZB
18 1 22 1.29 FALSE CB PL
19 0.907 11 0.547 FALSE CB RC
20 0.989 2 1.22 TRUE CB RT
21 0.913 11 1.07 TRUE GH WL
22 0.980 30 2.79 TRUE HN CB
23 0.993 30 2.81 TRUE HN HT
24 0.938 7 0.657 TRUE HN MI
25 0.988 316 2.11 TRUE HT AW
26 0.969 44 2.09 TRUE HT GH
27 1.00 93 2.53 TRUE HT LB
28 0.994 429 2.94 FALSE HT MI
29 0.891 3937 0.0459 FALSE HT NO
30 0.998 3 1.58 TRUE HT OK
31 0.915 21 1.88 TRUE HT PL
32 0.997 4 2.74 TRUE HT PM
33 0.997 37 1.88 TRUE HT RC
34 0.948 498 2.23 TRUE HT RT
35 0.905 31 1.55 FALSE HT WL
36 0.998 16 3.39 TRUE HT ZB
37 0.939 74 1.94 FALSE LB CB
38 0.942 11 1.50 TRUE LB MI
39 0.997 3 2.92 TRUE LB WL
40 0.988 114 4.15 TRUE MI AW
41 0.998 185 2.94 FALSE MI NO
42 0.962 8 3.38 TRUE MI OK
43 0.991 44 4.18 TRUE MI RT
44 0.988 2 3.49 TRUE MI ZB
45 0.902 11 1.59 FALSE NO WL
46 0.974 0 2.92 TRUE PH BT
47 0.972 0 2.79 TRUE PH CB
48 0.999 0 2.81 TRUE PH HT
49 0.909 0 0.657 TRUE PH MI
50 0.932 0 0.576 TRUE PH SI
51 0.894 8 1.12 TRUE RT WL
52 0.971 556 2.06 TRUE SI AD
53 0.986 622 4.01 TRUE SI AW
54 0.973 142 3.18 TRUE SI BT
55 0.982 18 3.18 TRUE SI CB
56 0.996 77 3.93 TRUE SI GH
57 0.958 182 0.576 TRUE SI HN
58 0.997 435 2.81 TRUE SI HT
59 0.966 395 1.55 TRUE SI LB
60 0.926 24 0.506 TRUE SI MI
61 0.998 383 2.80 TRUE SI NO
62 0.959 126 3.17 TRUE SI OK
63 0.998 112 3.86 TRUE SI PL
64 0.996 13 2.54 TRUE SI PM
65 0.967 60 2.94 TRUE SI RC
66 0.993 1055 4.05 TRUE SI RT
67 0.997 12 3.84 TRUE SI WL
68 0.986 207 3.51 TRUE SI ZB
69 0.996 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-304.1 -288.5 159.1 -318.1 62
Scaled residuals:
Min 1Q Median 3Q Max
-2.17103 -0.56904 -0.07609 0.74790 2.02501
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 3.154e-05 5.616e-03
PORT (Intercept) 6.547e-14 2.559e-07
Residual 5.536e-04 2.353e-02
Number of obs: 69, groups: DEST, 18; PORT, 14
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.268e-01 9.293e-03 99.734
PRED_TRIPS -8.491e-06 6.147e-06 -1.381
PRED_ENV 1.861e-02 2.906e-03 6.405
ECO_DIFFTRUE 1.448e-03 8.109e-03 0.179
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.349
PRED_ENV -0.566 0.098
ECO_DIFFTRU -0.615 0.229 -0.197
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 9.267885e-01 9.292588e-03 99.7341681
PRED_TRIPS -8.491409e-06 6.147101e-06 -1.3813682
PRED_ENV 1.861110e-02 2.905683e-03 6.4050684
ECO_DIFFTRUE 1.448431e-03 8.109489e-03 0.1786094
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -304.42 -291.01 158.21 -316.42
full_model 7 -304.14 -288.50 159.07 -318.14 1.7183 1 0.1899
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "15" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 64 6 .# A tibble: 64 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.955 10 1.45 TRUE AD BT
2 0.999 6 1.96 TRUE AD HT
3 0.984 2 2.15 TRUE AD WL
4 0.919 15 1.06 TRUE AW WL
5 0.847 189 1.56 TRUE BT AW
6 0.906 20 1.50 TRUE BT GH
7 0.972 11 2.92 TRUE BT HN
8 0.980 287 1.52 FALSE BT HT
9 0.936 26 2.14 TRUE BT LB
10 0.933 75 3.16 FALSE BT MI
11 0.971 221 1.56 FALSE BT NO
12 0.886 6 1.50 TRUE BT OK
13 0.981 17 1.41 TRUE BT PL
14 0.903 5 1.49 TRUE BT RC
15 0.927 83 1.57 TRUE BT RT
16 0.934 180 0.921 FALSE BT WL
17 0.931 94 2.25 TRUE BT ZB
18 0.998 22 1.29 FALSE CB PL
19 0.863 11 0.547 FALSE CB RC
20 0.970 2 1.22 TRUE CB RT
21 0.879 11 1.07 TRUE GH WL
22 0.947 30 2.79 TRUE HN CB
23 0.988 30 2.81 TRUE HN HT
24 0.899 7 0.657 TRUE HN MI
25 0.981 316 2.11 TRUE HT AW
26 0.951 44 2.09 TRUE HT GH
27 0.998 93 2.53 TRUE HT LB
28 0.987 429 2.94 FALSE HT MI
29 0.848 3937 0.0459 FALSE HT NO
30 0.991 3 1.58 TRUE HT OK
31 0.866 21 1.88 TRUE HT PL
32 0.995 4 2.74 TRUE HT PM
33 0.993 37 1.88 TRUE HT RC
34 0.920 498 2.23 TRUE HT RT
35 0.865 31 1.55 FALSE HT WL
36 0.997 16 3.39 TRUE HT ZB
37 0.901 74 1.94 FALSE LB CB
38 0.907 11 1.50 TRUE LB MI
39 0.991 3 2.92 TRUE LB WL
40 0.966 114 4.15 TRUE MI AW
41 0.990 185 2.94 FALSE MI NO
42 0.927 8 3.38 TRUE MI OK
43 0.976 44 4.18 TRUE MI RT
44 0.965 2 3.49 TRUE MI ZB
45 0.857 11 1.59 FALSE NO WL
46 0.839 8 1.12 TRUE RT WL
47 0.945 556 2.06 TRUE SI AD
48 0.960 622 4.01 TRUE SI AW
49 0.947 142 3.18 TRUE SI BT
50 0.950 18 3.18 TRUE SI CB
51 0.986 77 3.93 TRUE SI GH
52 0.935 182 0.576 TRUE SI HN
53 0.994 435 2.81 TRUE SI HT
54 0.931 395 1.55 TRUE SI LB
55 0.881 24 0.506 TRUE SI MI
56 0.995 383 2.80 TRUE SI NO
57 0.919 126 3.17 TRUE SI OK
58 0.997 112 3.86 TRUE SI PL
59 0.983 13 2.54 TRUE SI PM
60 0.928 60 2.94 TRUE SI RC
61 0.976 1055 4.05 TRUE SI RT
62 0.988 12 3.84 TRUE SI WL
63 0.952 207 3.51 TRUE SI ZB
64 0.987 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 64 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-224.4 -209.3 119.2 -238.4 57
Scaled residuals:
Min 1Q Median 3Q Max
-2.14414 -0.62543 -0.02483 0.75743 1.94434
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 7.293e-05 8.540e-03
PORT (Intercept) 6.941e-12 2.635e-06
Residual 1.345e-03 3.668e-02
Number of obs: 64, groups: DEST, 17; PORT, 13
Fixed effects:
Estimate Std. Error t value
(Intercept) 8.937e-01 1.472e-02 60.729
PRED_TRIPS -1.107e-05 9.603e-06 -1.153
PRED_ENV 2.341e-02 4.759e-03 4.920
ECO_DIFFTRUE 1.586e-03 1.278e-02 0.124
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.353
PRED_ENV -0.586 0.109
ECO_DIFFTRU -0.575 0.214 -0.222
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 8.936726e-01 1.471568e-02 60.7292745
PRED_TRIPS -1.107246e-05 9.603340e-06 -1.1529799
PRED_ENV 2.341112e-02 4.758759e-03 4.9195848
ECO_DIFFTRUE 1.585891e-03 1.278295e-02 0.1240629
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -225.21 -212.26 118.61 -237.21
full_model 7 -224.37 -209.26 119.19 -238.37 1.1632 1 0.2808
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "16" and formula index FIDX "1" in Summary Tables.Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 69 6 .# A tibble: 69 x 6
RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.955 10 1.45 TRUE AD BT
2 0.999 6 1.96 TRUE AD HT
3 0.984 2 2.15 TRUE AD WL
4 0.919 15 1.06 TRUE AW WL
5 0.847 189 1.56 TRUE BT AW
6 0.906 20 1.50 TRUE BT GH
7 0.972 11 2.92 TRUE BT HN
8 0.980 287 1.52 FALSE BT HT
9 0.936 26 2.14 TRUE BT LB
10 0.933 75 3.16 FALSE BT MI
11 0.971 221 1.56 FALSE BT NO
12 0.886 6 1.50 TRUE BT OK
13 0.981 17 1.41 TRUE BT PL
14 0.903 5 1.49 TRUE BT RC
15 0.927 83 1.57 TRUE BT RT
16 0.934 180 0.921 FALSE BT WL
17 0.931 94 2.25 TRUE BT ZB
18 0.998 22 1.29 FALSE CB PL
19 0.863 11 0.547 FALSE CB RC
20 0.970 2 1.22 TRUE CB RT
21 0.879 11 1.07 TRUE GH WL
22 0.947 30 2.79 TRUE HN CB
23 0.988 30 2.81 TRUE HN HT
24 0.899 7 0.657 TRUE HN MI
25 0.981 316 2.11 TRUE HT AW
26 0.951 44 2.09 TRUE HT GH
27 0.998 93 2.53 TRUE HT LB
28 0.987 429 2.94 FALSE HT MI
29 0.848 3937 0.0459 FALSE HT NO
30 0.991 3 1.58 TRUE HT OK
31 0.866 21 1.88 TRUE HT PL
32 0.995 4 2.74 TRUE HT PM
33 0.993 37 1.88 TRUE HT RC
34 0.920 498 2.23 TRUE HT RT
35 0.865 31 1.55 FALSE HT WL
36 0.997 16 3.39 TRUE HT ZB
37 0.901 74 1.94 FALSE LB CB
38 0.907 11 1.50 TRUE LB MI
39 0.991 3 2.92 TRUE LB WL
40 0.966 114 4.15 TRUE MI AW
41 0.990 185 2.94 FALSE MI NO
42 0.927 8 3.38 TRUE MI OK
43 0.976 44 4.18 TRUE MI RT
44 0.965 2 3.49 TRUE MI ZB
45 0.857 11 1.59 FALSE NO WL
46 0.945 0 2.92 TRUE PH BT
47 0.930 0 2.79 TRUE PH CB
48 0.998 0 2.81 TRUE PH HT
49 0.843 0 0.657 TRUE PH MI
50 0.898 0 0.576 TRUE PH SI
51 0.839 8 1.12 TRUE RT WL
52 0.945 556 2.06 TRUE SI AD
53 0.960 622 4.01 TRUE SI AW
54 0.947 142 3.18 TRUE SI BT
55 0.950 18 3.18 TRUE SI CB
56 0.986 77 3.93 TRUE SI GH
57 0.935 182 0.576 TRUE SI HN
58 0.994 435 2.81 TRUE SI HT
59 0.931 395 1.55 TRUE SI LB
60 0.881 24 0.506 TRUE SI MI
61 0.995 383 2.80 TRUE SI NO
62 0.919 126 3.17 TRUE SI OK
63 0.997 112 3.86 TRUE SI PL
64 0.983 13 2.54 TRUE SI PM
65 0.928 60 2.94 TRUE SI RC
66 0.976 1055 4.05 TRUE SI RT
67 0.988 12 3.84 TRUE SI WL
68 0.952 207 3.51 TRUE SI ZB
69 0.987 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 69 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-244.0 -228.4 129.0 -258.0 62
Scaled residuals:
Min 1Q Median 3Q Max
-2.12619 -0.58445 -0.05513 0.80749 1.93569
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001328 0.01152
PORT (Intercept) 0.0000000 0.00000
Residual 0.0012785 0.03576
Number of obs: 69, groups: DEST, 18; PORT, 14
Fixed effects:
Estimate Std. Error t value
(Intercept) 8.916e-01 1.453e-02 61.351
PRED_TRIPS -1.141e-05 9.497e-06 -1.201
PRED_ENV 2.491e-02 4.467e-03 5.576
ECO_DIFFTRUE -1.124e-03 1.247e-02 -0.090
Correlation of Fixed Effects:
(Intr) PRED_T PRED_E
PRED_TRIPS -0.345
PRED_ENV -0.567 0.106
ECO_DIFFTRU -0.616 0.221 -0.185
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.8916461761 1.453361e-02 61.35063380
PRED_TRIPS -0.0000114094 9.497040e-06 -1.20136431
PRED_ENV 0.0249085037 4.467148e-03 5.57592996
ECO_DIFFTRUE -0.0011238911 1.246941e-02 -0.09013187
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -244.68 -231.28 128.34 -256.68
full_model 7 -243.99 -228.36 129.00 -257.99 1.3103 1 0.2523
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "1" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.700 1 1.06 TRUE AW WL
2 0.636 163 1.56 TRUE BT AW
3 0.695 5 1.50 TRUE BT GH
4 0.794 304 1.52 FALSE BT HT
5 0.785 1 2.14 TRUE BT LB
6 0.756 67 3.16 FALSE BT MI
7 0.794 275 1.56 FALSE BT NO
8 0.732 60 1.57 TRUE BT RT
9 0.723 186 0.921 FALSE BT WL
10 0.776 126 2.25 TRUE BT ZB
11 0.832 16 1.29 FALSE CB PL
12 0.683 6 0.547 FALSE CB RC
13 0.654 1 1.07 TRUE GH WL
14 0.739 16 2.79 TRUE HN CB
15 0.829 1 2.81 TRUE HN HT
16 0.774 224 2.11 TRUE HT AW
17 0.747 8 2.09 TRUE HT GH
18 0.885 6 2.53 TRUE HT LB
19 0.845 209 2.94 FALSE HT MI
20 0.628 3684 0.0459 FALSE HT NO
21 0.828 1 2.74 TRUE HT PM
22 0.695 127 2.23 TRUE HT RT
23 0.639 13 1.55 FALSE HT WL
24 0.869 4 3.39 TRUE HT ZB
25 0.738 27 1.94 FALSE LB CB
26 0.726 1 1.50 TRUE LB MI
27 0.748 52 4.15 TRUE MI AW
28 0.864 92 2.94 FALSE MI NO
29 0.712 1 3.38 TRUE MI OK
30 0.786 1 4.18 TRUE MI RT
31 0.799 1 3.49 TRUE MI ZB
32 0.603 2 1.12 TRUE RT WL
33 0.815 117 2.06 TRUE SI AD
34 0.740 10 4.01 TRUE SI AW
35 0.748 3 3.18 TRUE SI BT
36 0.724 1 3.18 TRUE SI CB
37 0.789 7 3.93 TRUE SI GH
38 0.762 53 0.576 TRUE SI HN
39 0.836 53 2.81 TRUE SI HT
40 0.730 82 1.55 TRUE SI LB
41 0.853 65 2.80 TRUE SI NO
42 0.692 3 3.17 TRUE SI OK
43 0.835 14 3.86 TRUE SI PL
44 0.780 5 2.54 TRUE SI PM
45 0.691 19 2.94 TRUE SI RC
46 0.774 37 4.05 TRUE SI RT
47 0.789 2 3.51 TRUE SI ZB
48 0.817 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-139.2 -126.1 76.6 -153.2 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.9285 -0.4822 -0.0895 0.5744 2.0118
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 2.104e-03 0.045867
PORT (Intercept) 8.458e-05 0.009197
Residual 1.290e-03 0.035917
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.283e-01 2.397e-02 30.385
VOY_FREQ -4.318e-05 1.279e-05 -3.377
PRED_ENV 2.514e-02 7.356e-03 3.418
ECO_DIFFTRUE -2.728e-02 1.677e-02 -1.627
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.391
PRED_ENV -0.635 0.377
ECO_DIFFTRU -0.425 0.123 -0.172
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.282536e-01 2.396751e-02 30.385033
VOY_FREQ -4.318023e-05 1.278701e-05 -3.376883
PRED_ENV 2.514240e-02 7.356143e-03 3.417878
ECO_DIFFTRUE -2.727844e-02 1.676922e-02 -1.626698
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.05 -121.82 72.525 -145.05
full_model 7 -139.19 -126.09 76.593 -153.19 8.1376 1 0.004336 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "2" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.700 1 1.06 TRUE AW WL
2 0.636 163 1.56 TRUE BT AW
3 0.695 5 1.50 TRUE BT GH
4 0.794 304 1.52 FALSE BT HT
5 0.785 1 2.14 TRUE BT LB
6 0.756 67 3.16 FALSE BT MI
7 0.794 275 1.56 FALSE BT NO
8 0.732 60 1.57 TRUE BT RT
9 0.723 186 0.921 FALSE BT WL
10 0.776 126 2.25 TRUE BT ZB
11 0.832 16 1.29 FALSE CB PL
12 0.683 6 0.547 FALSE CB RC
13 0.654 1 1.07 TRUE GH WL
14 0.739 16 2.79 TRUE HN CB
15 0.829 1 2.81 TRUE HN HT
16 0.774 224 2.11 TRUE HT AW
17 0.747 8 2.09 TRUE HT GH
18 0.885 6 2.53 TRUE HT LB
19 0.845 209 2.94 FALSE HT MI
20 0.628 3684 0.0459 FALSE HT NO
21 0.828 1 2.74 TRUE HT PM
22 0.695 127 2.23 TRUE HT RT
23 0.639 13 1.55 FALSE HT WL
24 0.869 4 3.39 TRUE HT ZB
25 0.738 27 1.94 FALSE LB CB
26 0.726 1 1.50 TRUE LB MI
27 0.748 52 4.15 TRUE MI AW
28 0.864 92 2.94 FALSE MI NO
29 0.712 1 3.38 TRUE MI OK
30 0.786 1 4.18 TRUE MI RT
31 0.799 1 3.49 TRUE MI ZB
32 0.603 2 1.12 TRUE RT WL
33 0.815 117 2.06 TRUE SI AD
34 0.740 10 4.01 TRUE SI AW
35 0.748 3 3.18 TRUE SI BT
36 0.724 1 3.18 TRUE SI CB
37 0.789 7 3.93 TRUE SI GH
38 0.762 53 0.576 TRUE SI HN
39 0.836 53 2.81 TRUE SI HT
40 0.730 82 1.55 TRUE SI LB
41 0.853 65 2.80 TRUE SI NO
42 0.692 3 3.17 TRUE SI OK
43 0.835 14 3.86 TRUE SI PL
44 0.780 5 2.54 TRUE SI PM
45 0.691 19 2.94 TRUE SI RC
46 0.774 37 4.05 TRUE SI RT
47 0.789 2 3.51 TRUE SI ZB
48 0.817 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-139.2 -126.1 76.6 -153.2 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.9285 -0.4822 -0.0895 0.5744 2.0118
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 2.104e-03 0.045867
PORT (Intercept) 8.458e-05 0.009197
Residual 1.290e-03 0.035917
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.283e-01 2.397e-02 30.385
VOY_FREQ -4.318e-05 1.279e-05 -3.377
PRED_ENV 2.514e-02 7.356e-03 3.418
ECO_DIFFTRUE -2.728e-02 1.677e-02 -1.627
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.391
PRED_ENV -0.635 0.377
ECO_DIFFTRU -0.425 0.123 -0.172
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.282536e-01 2.396751e-02 30.385033
VOY_FREQ -4.318023e-05 1.278701e-05 -3.376883
PRED_ENV 2.514240e-02 7.356143e-03 3.417878
ECO_DIFFTRUE -2.727844e-02 1.676922e-02 -1.626698
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.05 -121.82 72.525 -145.05
full_model 7 -139.19 -126.09 76.593 -153.19 8.1376 1 0.004336 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "3" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.708 1 1.06 TRUE AW WL
2 0.634 163 1.56 TRUE BT AW
3 0.708 5 1.50 TRUE BT GH
4 0.800 304 1.52 FALSE BT HT
5 0.791 1 2.14 TRUE BT LB
6 0.751 67 3.16 FALSE BT MI
7 0.802 275 1.56 FALSE BT NO
8 0.732 60 1.57 TRUE BT RT
9 0.740 186 0.921 FALSE BT WL
10 0.786 126 2.25 TRUE BT ZB
11 0.831 16 1.29 FALSE CB PL
12 0.688 6 0.547 FALSE CB RC
13 0.671 1 1.07 TRUE GH WL
14 0.736 16 2.79 TRUE HN CB
15 0.830 1 2.81 TRUE HN HT
16 0.781 224 2.11 TRUE HT AW
17 0.753 8 2.09 TRUE HT GH
18 0.892 6 2.53 TRUE HT LB
19 0.851 209 2.94 FALSE HT MI
20 0.635 3684 0.0459 FALSE HT NO
21 0.824 1 2.74 TRUE HT PM
22 0.700 127 2.23 TRUE HT RT
23 0.644 13 1.55 FALSE HT WL
24 0.879 4 3.39 TRUE HT ZB
25 0.730 27 1.94 FALSE LB CB
26 0.726 1 1.50 TRUE LB MI
27 0.747 52 4.15 TRUE MI AW
28 0.870 92 2.94 FALSE MI NO
29 0.715 1 3.38 TRUE MI OK
30 0.789 1 4.18 TRUE MI RT
31 0.802 1 3.49 TRUE MI ZB
32 0.607 2 1.12 TRUE RT WL
33 0.815 117 2.06 TRUE SI AD
34 0.737 10 4.01 TRUE SI AW
35 0.747 3 3.18 TRUE SI BT
36 0.724 1 3.18 TRUE SI CB
37 0.790 7 3.93 TRUE SI GH
38 0.768 53 0.576 TRUE SI HN
39 0.837 53 2.81 TRUE SI HT
40 0.732 82 1.55 TRUE SI LB
41 0.851 65 2.80 TRUE SI NO
42 0.698 3 3.17 TRUE SI OK
43 0.836 14 3.86 TRUE SI PL
44 0.780 5 2.54 TRUE SI PM
45 0.702 19 2.94 TRUE SI RC
46 0.774 37 4.05 TRUE SI RT
47 0.786 2 3.51 TRUE SI ZB
48 0.821 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-136.7 -123.6 75.3 -150.7 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.94901 -0.45922 -0.01461 0.51781 2.03366
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 1.950e-03 0.044156
PORT (Intercept) 7.704e-05 0.008777
Residual 1.439e-03 0.037937
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.332e-01 2.443e-02 30.008
VOY_FREQ -4.176e-05 1.339e-05 -3.118
PRED_ENV 2.408e-02 7.616e-03 3.161
ECO_DIFFTRUE -2.718e-02 1.748e-02 -1.555
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.397
PRED_ENV -0.643 0.371
ECO_DIFFTRU -0.427 0.125 -0.183
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.331661e-01 2.443236e-02 30.008001
VOY_FREQ -4.175975e-05 1.339124e-05 -3.118439
PRED_ENV 2.407731e-02 7.616098e-03 3.161370
ECO_DIFFTRUE -2.717620e-02 1.748217e-02 -1.554509
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -131.56 -120.33 71.780 -143.56
full_model 7 -136.66 -123.56 75.332 -150.66 7.1038 1 0.007692 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients:
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "4" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.708 1 1.06 TRUE AW WL
2 0.634 163 1.56 TRUE BT AW
3 0.708 5 1.50 TRUE BT GH
4 0.800 304 1.52 FALSE BT HT
5 0.791 1 2.14 TRUE BT LB
6 0.751 67 3.16 FALSE BT MI
7 0.802 275 1.56 FALSE BT NO
8 0.732 60 1.57 TRUE BT RT
9 0.740 186 0.921 FALSE BT WL
10 0.786 126 2.25 TRUE BT ZB
11 0.831 16 1.29 FALSE CB PL
12 0.688 6 0.547 FALSE CB RC
13 0.671 1 1.07 TRUE GH WL
14 0.736 16 2.79 TRUE HN CB
15 0.830 1 2.81 TRUE HN HT
16 0.781 224 2.11 TRUE HT AW
17 0.753 8 2.09 TRUE HT GH
18 0.892 6 2.53 TRUE HT LB
19 0.851 209 2.94 FALSE HT MI
20 0.635 3684 0.0459 FALSE HT NO
21 0.824 1 2.74 TRUE HT PM
22 0.700 127 2.23 TRUE HT RT
23 0.644 13 1.55 FALSE HT WL
24 0.879 4 3.39 TRUE HT ZB
25 0.730 27 1.94 FALSE LB CB
26 0.726 1 1.50 TRUE LB MI
27 0.747 52 4.15 TRUE MI AW
28 0.870 92 2.94 FALSE MI NO
29 0.715 1 3.38 TRUE MI OK
30 0.789 1 4.18 TRUE MI RT
31 0.802 1 3.49 TRUE MI ZB
32 0.607 2 1.12 TRUE RT WL
33 0.815 117 2.06 TRUE SI AD
34 0.737 10 4.01 TRUE SI AW
35 0.747 3 3.18 TRUE SI BT
36 0.724 1 3.18 TRUE SI CB
37 0.790 7 3.93 TRUE SI GH
38 0.768 53 0.576 TRUE SI HN
39 0.837 53 2.81 TRUE SI HT
40 0.732 82 1.55 TRUE SI LB
41 0.851 65 2.80 TRUE SI NO
42 0.698 3 3.17 TRUE SI OK
43 0.836 14 3.86 TRUE SI PL
44 0.780 5 2.54 TRUE SI PM
45 0.702 19 2.94 TRUE SI RC
46 0.774 37 4.05 TRUE SI RT
47 0.786 2 3.51 TRUE SI ZB
48 0.821 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-136.7 -123.6 75.3 -150.7 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.94901 -0.45922 -0.01461 0.51781 2.03366
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 1.950e-03 0.044156
PORT (Intercept) 7.704e-05 0.008777
Residual 1.439e-03 0.037937
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.332e-01 2.443e-02 30.008
VOY_FREQ -4.176e-05 1.339e-05 -3.118
PRED_ENV 2.408e-02 7.616e-03 3.161
ECO_DIFFTRUE -2.718e-02 1.748e-02 -1.555
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.397
PRED_ENV -0.643 0.371
ECO_DIFFTRU -0.427 0.125 -0.183
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.331661e-01 2.443236e-02 30.008001
VOY_FREQ -4.175975e-05 1.339124e-05 -3.118439
PRED_ENV 2.407731e-02 7.616098e-03 3.161370
ECO_DIFFTRUE -2.717620e-02 1.748217e-02 -1.554509
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -131.56 -120.33 71.780 -143.56
full_model 7 -136.66 -123.56 75.332 -150.66 7.1038 1 0.007692 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "5" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.950 1 1.06 TRUE AW WL
2 0.902 163 1.56 TRUE BT AW
3 0.942 5 1.50 TRUE BT GH
4 0.989 304 1.52 FALSE BT HT
5 0.964 1 2.14 TRUE BT LB
6 0.974 67 3.16 FALSE BT MI
7 0.985 275 1.56 FALSE BT NO
8 0.955 60 1.57 TRUE BT RT
9 0.958 186 0.921 FALSE BT WL
10 0.955 126 2.25 TRUE BT ZB
11 1 16 1.29 FALSE CB PL
12 0.908 6 0.547 FALSE CB RC
13 0.913 1 1.07 TRUE GH WL
14 0.980 16 2.79 TRUE HN CB
15 0.993 1 2.81 TRUE HN HT
16 0.988 224 2.11 TRUE HT AW
17 0.969 8 2.09 TRUE HT GH
18 1.00 6 2.53 TRUE HT LB
19 0.994 209 2.94 FALSE HT MI
20 0.892 3684 0.0459 FALSE HT NO
21 0.997 1 2.74 TRUE HT PM
22 0.948 127 2.23 TRUE HT RT
23 0.906 13 1.55 FALSE HT WL
24 0.999 4 3.39 TRUE HT ZB
25 0.940 27 1.94 FALSE LB CB
26 0.943 1 1.50 TRUE LB MI
27 0.988 52 4.15 TRUE MI AW
28 0.998 92 2.94 FALSE MI NO
29 0.963 1 3.38 TRUE MI OK
30 0.991 1 4.18 TRUE MI RT
31 0.988 1 3.49 TRUE MI ZB
32 0.894 2 1.12 TRUE RT WL
33 0.971 117 2.06 TRUE SI AD
34 0.985 10 4.01 TRUE SI AW
35 0.973 3 3.18 TRUE SI BT
36 0.981 1 3.18 TRUE SI CB
37 0.995 7 3.93 TRUE SI GH
38 0.959 53 0.576 TRUE SI HN
39 0.997 53 2.81 TRUE SI HT
40 0.967 82 1.55 TRUE SI LB
41 0.997 65 2.80 TRUE SI NO
42 0.958 3 3.17 TRUE SI OK
43 0.998 14 3.86 TRUE SI PL
44 0.996 5 2.54 TRUE SI PM
45 0.965 19 2.94 TRUE SI RC
46 0.992 37 4.05 TRUE SI RT
47 0.984 2 3.51 TRUE SI ZB
48 0.996 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-220.0 -206.9 117.0 -234.0 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.29190 -0.46783 -0.00733 0.61421 1.88346
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001526 0.01235
PORT (Intercept) 0.0000000 0.00000
Residual 0.0003375 0.01837
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.351e-01 9.819e-03 95.236
VOY_FREQ -1.363e-05 6.112e-06 -2.230
PRED_ENV 1.770e-02 3.277e-03 5.403
ECO_DIFFTRUE -8.485e-03 7.770e-03 -1.092
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.431
PRED_ENV -0.667 0.336
ECO_DIFFTRU -0.424 0.148 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 9.350964e-01 9.818769e-03 95.235598
VOY_FREQ -1.363075e-05 6.111967e-06 -2.230174
PRED_ENV 1.770408e-02 3.277001e-03 5.402524
ECO_DIFFTRUE -8.485289e-03 7.770235e-03 -1.092025
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.82 -206.59 114.91 -229.82
full_model 7 -220.05 -206.95 117.02 -234.05 4.2277 1 0.03977 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "6" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.950 1 1.06 TRUE AW WL
2 0.902 163 1.56 TRUE BT AW
3 0.942 5 1.50 TRUE BT GH
4 0.989 304 1.52 FALSE BT HT
5 0.964 1 2.14 TRUE BT LB
6 0.974 67 3.16 FALSE BT MI
7 0.985 275 1.56 FALSE BT NO
8 0.955 60 1.57 TRUE BT RT
9 0.958 186 0.921 FALSE BT WL
10 0.955 126 2.25 TRUE BT ZB
11 1 16 1.29 FALSE CB PL
12 0.908 6 0.547 FALSE CB RC
13 0.913 1 1.07 TRUE GH WL
14 0.980 16 2.79 TRUE HN CB
15 0.993 1 2.81 TRUE HN HT
16 0.988 224 2.11 TRUE HT AW
17 0.969 8 2.09 TRUE HT GH
18 1.00 6 2.53 TRUE HT LB
19 0.994 209 2.94 FALSE HT MI
20 0.892 3684 0.0459 FALSE HT NO
21 0.997 1 2.74 TRUE HT PM
22 0.948 127 2.23 TRUE HT RT
23 0.906 13 1.55 FALSE HT WL
24 0.999 4 3.39 TRUE HT ZB
25 0.940 27 1.94 FALSE LB CB
26 0.943 1 1.50 TRUE LB MI
27 0.988 52 4.15 TRUE MI AW
28 0.998 92 2.94 FALSE MI NO
29 0.963 1 3.38 TRUE MI OK
30 0.991 1 4.18 TRUE MI RT
31 0.988 1 3.49 TRUE MI ZB
32 0.894 2 1.12 TRUE RT WL
33 0.971 117 2.06 TRUE SI AD
34 0.985 10 4.01 TRUE SI AW
35 0.973 3 3.18 TRUE SI BT
36 0.981 1 3.18 TRUE SI CB
37 0.995 7 3.93 TRUE SI GH
38 0.959 53 0.576 TRUE SI HN
39 0.997 53 2.81 TRUE SI HT
40 0.967 82 1.55 TRUE SI LB
41 0.997 65 2.80 TRUE SI NO
42 0.958 3 3.17 TRUE SI OK
43 0.998 14 3.86 TRUE SI PL
44 0.996 5 2.54 TRUE SI PM
45 0.965 19 2.94 TRUE SI RC
46 0.992 37 4.05 TRUE SI RT
47 0.984 2 3.51 TRUE SI ZB
48 0.996 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-220.0 -206.9 117.0 -234.0 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.29190 -0.46783 -0.00733 0.61421 1.88346
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001526 0.01235
PORT (Intercept) 0.0000000 0.00000
Residual 0.0003375 0.01837
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.351e-01 9.819e-03 95.236
VOY_FREQ -1.363e-05 6.112e-06 -2.230
PRED_ENV 1.770e-02 3.277e-03 5.403
ECO_DIFFTRUE -8.485e-03 7.770e-03 -1.092
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.431
PRED_ENV -0.667 0.336
ECO_DIFFTRU -0.424 0.148 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 9.350964e-01 9.818769e-03 95.235598
VOY_FREQ -1.363075e-05 6.111967e-06 -2.230174
PRED_ENV 1.770408e-02 3.277001e-03 5.402524
ECO_DIFFTRUE -8.485289e-03 7.770235e-03 -1.092025
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.82 -206.59 114.91 -229.82
full_model 7 -220.05 -206.95 117.02 -234.05 4.2277 1 0.03977 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "7" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 1 1.06 TRUE AW WL
2 0.848 163 1.56 TRUE BT AW
3 0.907 5 1.50 TRUE BT GH
4 0.980 304 1.52 FALSE BT HT
5 0.938 1 2.14 TRUE BT LB
6 0.934 67 3.16 FALSE BT MI
7 0.971 275 1.56 FALSE BT NO
8 0.928 60 1.57 TRUE BT RT
9 0.935 186 0.921 FALSE BT WL
10 0.932 126 2.25 TRUE BT ZB
11 0.998 16 1.29 FALSE CB PL
12 0.863 6 0.547 FALSE CB RC
13 0.879 1 1.07 TRUE GH WL
14 0.948 16 2.79 TRUE HN CB
15 0.988 1 2.81 TRUE HN HT
16 0.981 224 2.11 TRUE HT AW
17 0.951 8 2.09 TRUE HT GH
18 0.998 6 2.53 TRUE HT LB
19 0.987 209 2.94 FALSE HT MI
20 0.848 3684 0.0459 FALSE HT NO
21 0.995 1 2.74 TRUE HT PM
22 0.921 127 2.23 TRUE HT RT
23 0.866 13 1.55 FALSE HT WL
24 0.997 4 3.39 TRUE HT ZB
25 0.902 27 1.94 FALSE LB CB
26 0.908 1 1.50 TRUE LB MI
27 0.967 52 4.15 TRUE MI AW
28 0.990 92 2.94 FALSE MI NO
29 0.928 1 3.38 TRUE MI OK
30 0.977 1 4.18 TRUE MI RT
31 0.966 1 3.49 TRUE MI ZB
32 0.840 2 1.12 TRUE RT WL
33 0.946 117 2.06 TRUE SI AD
34 0.960 10 4.01 TRUE SI AW
35 0.947 3 3.18 TRUE SI BT
36 0.950 1 3.18 TRUE SI CB
37 0.986 7 3.93 TRUE SI GH
38 0.935 53 0.576 TRUE SI HN
39 0.993 53 2.81 TRUE SI HT
40 0.932 82 1.55 TRUE SI LB
41 0.994 65 2.80 TRUE SI NO
42 0.922 3 3.17 TRUE SI OK
43 0.996 14 3.86 TRUE SI PL
44 0.983 5 2.54 TRUE SI PM
45 0.930 19 2.94 TRUE SI RC
46 0.976 37 4.05 TRUE SI RT
47 0.952 2 3.51 TRUE SI ZB
48 0.987 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-179.0 -165.9 96.5 -193.0 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.30511 -0.46831 -0.09867 0.67532 2.08860
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 4.148e-04 2.037e-02
PORT (Intercept) 4.686e-12 2.165e-06
Residual 7.653e-04 2.766e-02
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.040e-01 1.507e-02 59.997
VOY_FREQ -1.878e-05 9.269e-06 -2.027
PRED_ENV 2.280e-02 4.991e-03 4.568
ECO_DIFFTRUE -1.259e-02 1.181e-02 -1.066
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.426
PRED_ENV -0.663 0.341
ECO_DIFFTRU -0.422 0.142 -0.251
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.9040493388 1.506822e-02 59.997088
VOY_FREQ -0.0000187841 9.268835e-06 -2.026587
PRED_ENV 0.0228014076 4.991360e-03 4.568176
ECO_DIFFTRUE -0.0125892017 1.181367e-02 -1.065647
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -177.49 -166.26 94.743 -189.49
full_model 7 -179.05 -165.95 96.523 -193.05 3.5598 1 0.05919 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "8" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 1 1.06 TRUE AW WL
2 0.848 163 1.56 TRUE BT AW
3 0.907 5 1.50 TRUE BT GH
4 0.980 304 1.52 FALSE BT HT
5 0.938 1 2.14 TRUE BT LB
6 0.934 67 3.16 FALSE BT MI
7 0.971 275 1.56 FALSE BT NO
8 0.928 60 1.57 TRUE BT RT
9 0.935 186 0.921 FALSE BT WL
10 0.932 126 2.25 TRUE BT ZB
11 0.998 16 1.29 FALSE CB PL
12 0.863 6 0.547 FALSE CB RC
13 0.879 1 1.07 TRUE GH WL
14 0.948 16 2.79 TRUE HN CB
15 0.988 1 2.81 TRUE HN HT
16 0.981 224 2.11 TRUE HT AW
17 0.951 8 2.09 TRUE HT GH
18 0.998 6 2.53 TRUE HT LB
19 0.987 209 2.94 FALSE HT MI
20 0.848 3684 0.0459 FALSE HT NO
21 0.995 1 2.74 TRUE HT PM
22 0.921 127 2.23 TRUE HT RT
23 0.866 13 1.55 FALSE HT WL
24 0.997 4 3.39 TRUE HT ZB
25 0.902 27 1.94 FALSE LB CB
26 0.908 1 1.50 TRUE LB MI
27 0.967 52 4.15 TRUE MI AW
28 0.990 92 2.94 FALSE MI NO
29 0.928 1 3.38 TRUE MI OK
30 0.977 1 4.18 TRUE MI RT
31 0.966 1 3.49 TRUE MI ZB
32 0.840 2 1.12 TRUE RT WL
33 0.946 117 2.06 TRUE SI AD
34 0.960 10 4.01 TRUE SI AW
35 0.947 3 3.18 TRUE SI BT
36 0.950 1 3.18 TRUE SI CB
37 0.986 7 3.93 TRUE SI GH
38 0.935 53 0.576 TRUE SI HN
39 0.993 53 2.81 TRUE SI HT
40 0.932 82 1.55 TRUE SI LB
41 0.994 65 2.80 TRUE SI NO
42 0.922 3 3.17 TRUE SI OK
43 0.996 14 3.86 TRUE SI PL
44 0.983 5 2.54 TRUE SI PM
45 0.930 19 2.94 TRUE SI RC
46 0.976 37 4.05 TRUE SI RT
47 0.952 2 3.51 TRUE SI ZB
48 0.987 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-179.0 -165.9 96.5 -193.0 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.30511 -0.46831 -0.09867 0.67532 2.08860
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 4.148e-04 2.037e-02
PORT (Intercept) 4.686e-12 2.165e-06
Residual 7.653e-04 2.766e-02
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.040e-01 1.507e-02 59.997
VOY_FREQ -1.878e-05 9.269e-06 -2.027
PRED_ENV 2.280e-02 4.991e-03 4.568
ECO_DIFFTRUE -1.259e-02 1.181e-02 -1.066
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.426
PRED_ENV -0.663 0.341
ECO_DIFFTRU -0.422 0.142 -0.251
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.9040493388 1.506822e-02 59.997088
VOY_FREQ -0.0000187841 9.268835e-06 -2.026587
PRED_ENV 0.0228014076 4.991360e-03 4.568176
ECO_DIFFTRUE -0.0125892017 1.181367e-02 -1.065647
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -177.49 -166.26 94.743 -189.49
full_model 7 -179.05 -165.95 96.523 -193.05 3.5598 1 0.05919 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "9" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.702 1 1.06 TRUE AW WL
2 0.634 163 1.56 TRUE BT AW
3 0.713 5 1.50 TRUE BT GH
4 0.800 304 1.52 FALSE BT HT
5 0.782 1 2.14 TRUE BT LB
6 0.760 67 3.16 FALSE BT MI
7 0.801 275 1.56 FALSE BT NO
8 0.728 60 1.57 TRUE BT RT
9 0.728 186 0.921 FALSE BT WL
10 0.764 126 2.25 TRUE BT ZB
11 0.836 16 1.29 FALSE CB PL
12 0.683 6 0.547 FALSE CB RC
13 0.673 1 1.07 TRUE GH WL
14 0.741 16 2.79 TRUE HN CB
15 0.840 1 2.81 TRUE HN HT
16 0.779 224 2.11 TRUE HT AW
17 0.765 8 2.09 TRUE HT GH
18 0.889 6 2.53 TRUE HT LB
19 0.849 209 2.94 FALSE HT MI
20 0.632 3684 0.0459 FALSE HT NO
21 0.838 1 2.74 TRUE HT PM
22 0.701 127 2.23 TRUE HT RT
23 0.653 13 1.55 FALSE HT WL
24 0.874 4 3.39 TRUE HT ZB
25 0.735 27 1.94 FALSE LB CB
26 0.721 1 1.50 TRUE LB MI
27 0.745 52 4.15 TRUE MI AW
28 0.869 92 2.94 FALSE MI NO
29 0.719 1 3.38 TRUE MI OK
30 0.785 1 4.18 TRUE MI RT
31 0.790 1 3.49 TRUE MI ZB
32 0.603 2 1.12 TRUE RT WL
33 0.796 117 2.06 TRUE SI AD
34 0.741 10 4.01 TRUE SI AW
35 0.743 3 3.18 TRUE SI BT
36 0.737 1 3.18 TRUE SI CB
37 0.797 7 3.93 TRUE SI GH
38 0.761 53 0.576 TRUE SI HN
39 0.842 53 2.81 TRUE SI HT
40 0.727 82 1.55 TRUE SI LB
41 0.858 65 2.80 TRUE SI NO
42 0.706 3 3.17 TRUE SI OK
43 0.841 14 3.86 TRUE SI PL
44 0.781 5 2.54 TRUE SI PM
45 0.699 19 2.94 TRUE SI RC
46 0.777 37 4.05 TRUE SI RT
47 0.777 2 3.51 TRUE SI ZB
48 0.813 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-139.5 -126.4 76.8 -153.5 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.99915 -0.41205 -0.09242 0.58291 1.93039
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0019784 0.04448
PORT (Intercept) 0.0001904 0.01380
Residual 0.0012387 0.03520
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.274e-01 2.474e-02 29.401
VOY_FREQ -4.568e-05 1.275e-05 -3.581
PRED_ENV 2.649e-02 7.604e-03 3.484
ECO_DIFFTRUE -2.653e-02 1.693e-02 -1.567
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.393
PRED_ENV -0.648 0.387
ECO_DIFFTRU -0.450 0.134 -0.120
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.274389e-01 2.474218e-02 29.400760
VOY_FREQ -4.567822e-05 1.275484e-05 -3.581247
PRED_ENV 2.649350e-02 7.603665e-03 3.484306
ECO_DIFFTRUE -2.653286e-02 1.693410e-02 -1.566830
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.12 -121.90 72.563 -145.12
full_model 7 -139.54 -126.44 76.770 -153.54 8.4143 1 0.003723 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients:
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "10" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.702 1 1.06 TRUE AW WL
2 0.634 163 1.56 TRUE BT AW
3 0.713 5 1.50 TRUE BT GH
4 0.800 304 1.52 FALSE BT HT
5 0.782 1 2.14 TRUE BT LB
6 0.760 67 3.16 FALSE BT MI
7 0.801 275 1.56 FALSE BT NO
8 0.728 60 1.57 TRUE BT RT
9 0.728 186 0.921 FALSE BT WL
10 0.764 126 2.25 TRUE BT ZB
11 0.836 16 1.29 FALSE CB PL
12 0.683 6 0.547 FALSE CB RC
13 0.673 1 1.07 TRUE GH WL
14 0.741 16 2.79 TRUE HN CB
15 0.840 1 2.81 TRUE HN HT
16 0.779 224 2.11 TRUE HT AW
17 0.765 8 2.09 TRUE HT GH
18 0.889 6 2.53 TRUE HT LB
19 0.849 209 2.94 FALSE HT MI
20 0.632 3684 0.0459 FALSE HT NO
21 0.838 1 2.74 TRUE HT PM
22 0.701 127 2.23 TRUE HT RT
23 0.653 13 1.55 FALSE HT WL
24 0.874 4 3.39 TRUE HT ZB
25 0.735 27 1.94 FALSE LB CB
26 0.721 1 1.50 TRUE LB MI
27 0.745 52 4.15 TRUE MI AW
28 0.869 92 2.94 FALSE MI NO
29 0.719 1 3.38 TRUE MI OK
30 0.785 1 4.18 TRUE MI RT
31 0.790 1 3.49 TRUE MI ZB
32 0.603 2 1.12 TRUE RT WL
33 0.796 117 2.06 TRUE SI AD
34 0.741 10 4.01 TRUE SI AW
35 0.743 3 3.18 TRUE SI BT
36 0.737 1 3.18 TRUE SI CB
37 0.797 7 3.93 TRUE SI GH
38 0.761 53 0.576 TRUE SI HN
39 0.842 53 2.81 TRUE SI HT
40 0.727 82 1.55 TRUE SI LB
41 0.858 65 2.80 TRUE SI NO
42 0.706 3 3.17 TRUE SI OK
43 0.841 14 3.86 TRUE SI PL
44 0.781 5 2.54 TRUE SI PM
45 0.699 19 2.94 TRUE SI RC
46 0.777 37 4.05 TRUE SI RT
47 0.777 2 3.51 TRUE SI ZB
48 0.813 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-139.5 -126.4 76.8 -153.5 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.99915 -0.41205 -0.09242 0.58291 1.93039
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0019784 0.04448
PORT (Intercept) 0.0001904 0.01380
Residual 0.0012387 0.03520
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.274e-01 2.474e-02 29.401
VOY_FREQ -4.568e-05 1.275e-05 -3.581
PRED_ENV 2.649e-02 7.604e-03 3.484
ECO_DIFFTRUE -2.653e-02 1.693e-02 -1.567
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.393
PRED_ENV -0.648 0.387
ECO_DIFFTRU -0.450 0.134 -0.120
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.274389e-01 2.474218e-02 29.400760
VOY_FREQ -4.567822e-05 1.275484e-05 -3.581247
PRED_ENV 2.649350e-02 7.603665e-03 3.484306
ECO_DIFFTRUE -2.653286e-02 1.693410e-02 -1.566830
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.12 -121.90 72.563 -145.12
full_model 7 -139.54 -126.44 76.770 -153.54 8.4143 1 0.003723 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "11" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.716 1 1.06 TRUE AW WL
2 0.625 163 1.56 TRUE BT AW
3 0.705 5 1.50 TRUE BT GH
4 0.800 304 1.52 FALSE BT HT
5 0.777 1 2.14 TRUE BT LB
6 0.750 67 3.16 FALSE BT MI
7 0.795 275 1.56 FALSE BT NO
8 0.735 60 1.57 TRUE BT RT
9 0.733 186 0.921 FALSE BT WL
10 0.776 126 2.25 TRUE BT ZB
11 0.840 16 1.29 FALSE CB PL
12 0.675 6 0.547 FALSE CB RC
13 0.675 1 1.07 TRUE GH WL
14 0.731 16 2.79 TRUE HN CB
15 0.836 1 2.81 TRUE HN HT
16 0.781 224 2.11 TRUE HT AW
17 0.757 8 2.09 TRUE HT GH
18 0.891 6 2.53 TRUE HT LB
19 0.852 209 2.94 FALSE HT MI
20 0.619 3684 0.0459 FALSE HT NO
21 0.830 1 2.74 TRUE HT PM
22 0.694 127 2.23 TRUE HT RT
23 0.642 13 1.55 FALSE HT WL
24 0.878 4 3.39 TRUE HT ZB
25 0.724 27 1.94 FALSE LB CB
26 0.710 1 1.50 TRUE LB MI
27 0.745 52 4.15 TRUE MI AW
28 0.870 92 2.94 FALSE MI NO
29 0.706 1 3.38 TRUE MI OK
30 0.793 1 4.18 TRUE MI RT
31 0.801 1 3.49 TRUE MI ZB
32 0.607 2 1.12 TRUE RT WL
33 0.805 117 2.06 TRUE SI AD
34 0.734 10 4.01 TRUE SI AW
35 0.741 3 3.18 TRUE SI BT
36 0.736 1 3.18 TRUE SI CB
37 0.800 7 3.93 TRUE SI GH
38 0.765 53 0.576 TRUE SI HN
39 0.843 53 2.81 TRUE SI HT
40 0.721 82 1.55 TRUE SI LB
41 0.857 65 2.80 TRUE SI NO
42 0.706 3 3.17 TRUE SI OK
43 0.843 14 3.86 TRUE SI PL
44 0.784 5 2.54 TRUE SI PM
45 0.692 19 2.94 TRUE SI RC
46 0.777 37 4.05 TRUE SI RT
47 0.777 2 3.51 TRUE SI ZB
48 0.830 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-131.9 -118.8 72.9 -145.9 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.93157 -0.46344 -0.07663 0.61013 2.03076
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0019728 0.044416
PORT (Intercept) 0.0000957 0.009782
Residual 0.0016357 0.040444
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.253e-01 2.575e-02 28.164
VOY_FREQ -4.423e-05 1.425e-05 -3.104
PRED_ENV 2.646e-02 8.096e-03 3.269
ECO_DIFFTRUE -2.632e-02 1.859e-02 -1.416
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.402
PRED_ENV -0.649 0.370
ECO_DIFFTRU -0.432 0.129 -0.180
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.253403e-01 2.575375e-02 28.164451
VOY_FREQ -4.422621e-05 1.424872e-05 -3.103873
PRED_ENV 2.646313e-02 8.095769e-03 3.268760
ECO_DIFFTRUE -2.631902e-02 1.858502e-02 -1.416141
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -126.93 -115.70 69.464 -138.93
full_model 7 -131.86 -118.76 72.930 -145.86 6.9323 1 0.008465 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients:
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "12" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.716 1 1.06 TRUE AW WL
2 0.625 163 1.56 TRUE BT AW
3 0.705 5 1.50 TRUE BT GH
4 0.800 304 1.52 FALSE BT HT
5 0.777 1 2.14 TRUE BT LB
6 0.750 67 3.16 FALSE BT MI
7 0.795 275 1.56 FALSE BT NO
8 0.735 60 1.57 TRUE BT RT
9 0.733 186 0.921 FALSE BT WL
10 0.776 126 2.25 TRUE BT ZB
11 0.840 16 1.29 FALSE CB PL
12 0.675 6 0.547 FALSE CB RC
13 0.675 1 1.07 TRUE GH WL
14 0.731 16 2.79 TRUE HN CB
15 0.836 1 2.81 TRUE HN HT
16 0.781 224 2.11 TRUE HT AW
17 0.757 8 2.09 TRUE HT GH
18 0.891 6 2.53 TRUE HT LB
19 0.852 209 2.94 FALSE HT MI
20 0.619 3684 0.0459 FALSE HT NO
21 0.830 1 2.74 TRUE HT PM
22 0.694 127 2.23 TRUE HT RT
23 0.642 13 1.55 FALSE HT WL
24 0.878 4 3.39 TRUE HT ZB
25 0.724 27 1.94 FALSE LB CB
26 0.710 1 1.50 TRUE LB MI
27 0.745 52 4.15 TRUE MI AW
28 0.870 92 2.94 FALSE MI NO
29 0.706 1 3.38 TRUE MI OK
30 0.793 1 4.18 TRUE MI RT
31 0.801 1 3.49 TRUE MI ZB
32 0.607 2 1.12 TRUE RT WL
33 0.805 117 2.06 TRUE SI AD
34 0.734 10 4.01 TRUE SI AW
35 0.741 3 3.18 TRUE SI BT
36 0.736 1 3.18 TRUE SI CB
37 0.800 7 3.93 TRUE SI GH
38 0.765 53 0.576 TRUE SI HN
39 0.843 53 2.81 TRUE SI HT
40 0.721 82 1.55 TRUE SI LB
41 0.857 65 2.80 TRUE SI NO
42 0.706 3 3.17 TRUE SI OK
43 0.843 14 3.86 TRUE SI PL
44 0.784 5 2.54 TRUE SI PM
45 0.692 19 2.94 TRUE SI RC
46 0.777 37 4.05 TRUE SI RT
47 0.777 2 3.51 TRUE SI ZB
48 0.830 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-131.9 -118.8 72.9 -145.9 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.93157 -0.46344 -0.07663 0.61013 2.03076
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0019728 0.044416
PORT (Intercept) 0.0000957 0.009782
Residual 0.0016357 0.040444
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.253e-01 2.575e-02 28.164
VOY_FREQ -4.423e-05 1.425e-05 -3.104
PRED_ENV 2.646e-02 8.096e-03 3.269
ECO_DIFFTRUE -2.632e-02 1.859e-02 -1.416
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.402
PRED_ENV -0.649 0.370
ECO_DIFFTRU -0.432 0.129 -0.180
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 7.253403e-01 2.575375e-02 28.164451
VOY_FREQ -4.422621e-05 1.424872e-05 -3.103873
PRED_ENV 2.646313e-02 8.095769e-03 3.268760
ECO_DIFFTRUE -2.631902e-02 1.858502e-02 -1.416141
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -126.93 -115.70 69.464 -138.93
full_model 7 -131.86 -118.76 72.930 -145.86 6.9323 1 0.008465 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "13" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.951 1 1.06 TRUE AW WL
2 0.902 163 1.56 TRUE BT AW
3 0.943 5 1.50 TRUE BT GH
4 0.989 304 1.52 FALSE BT HT
5 0.964 1 2.14 TRUE BT LB
6 0.974 67 3.16 FALSE BT MI
7 0.985 275 1.56 FALSE BT NO
8 0.955 60 1.57 TRUE BT RT
9 0.957 186 0.921 FALSE BT WL
10 0.956 126 2.25 TRUE BT ZB
11 1 16 1.29 FALSE CB PL
12 0.907 6 0.547 FALSE CB RC
13 0.913 1 1.07 TRUE GH WL
14 0.980 16 2.79 TRUE HN CB
15 0.993 1 2.81 TRUE HN HT
16 0.988 224 2.11 TRUE HT AW
17 0.969 8 2.09 TRUE HT GH
18 1.00 6 2.53 TRUE HT LB
19 0.994 209 2.94 FALSE HT MI
20 0.891 3684 0.0459 FALSE HT NO
21 0.997 1 2.74 TRUE HT PM
22 0.948 127 2.23 TRUE HT RT
23 0.905 13 1.55 FALSE HT WL
24 0.998 4 3.39 TRUE HT ZB
25 0.939 27 1.94 FALSE LB CB
26 0.942 1 1.50 TRUE LB MI
27 0.988 52 4.15 TRUE MI AW
28 0.998 92 2.94 FALSE MI NO
29 0.962 1 3.38 TRUE MI OK
30 0.991 1 4.18 TRUE MI RT
31 0.988 1 3.49 TRUE MI ZB
32 0.894 2 1.12 TRUE RT WL
33 0.971 117 2.06 TRUE SI AD
34 0.986 10 4.01 TRUE SI AW
35 0.973 3 3.18 TRUE SI BT
36 0.982 1 3.18 TRUE SI CB
37 0.996 7 3.93 TRUE SI GH
38 0.958 53 0.576 TRUE SI HN
39 0.997 53 2.81 TRUE SI HT
40 0.966 82 1.55 TRUE SI LB
41 0.998 65 2.80 TRUE SI NO
42 0.959 3 3.17 TRUE SI OK
43 0.998 14 3.86 TRUE SI PL
44 0.996 5 2.54 TRUE SI PM
45 0.967 19 2.94 TRUE SI RC
46 0.993 37 4.05 TRUE SI RT
47 0.986 2 3.51 TRUE SI ZB
48 0.996 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-219.7 -206.6 116.9 -233.7 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.30113 -0.47225 -0.00788 0.61214 1.87274
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001487 0.0122
PORT (Intercept) 0.0000000 0.0000
Residual 0.0003422 0.0185
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.342e-01 9.847e-03 94.873
VOY_FREQ -1.361e-05 6.145e-06 -2.215
PRED_ENV 1.797e-02 3.291e-03 5.459
ECO_DIFFTRUE -8.052e-03 7.807e-03 -1.031
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.432
PRED_ENV -0.668 0.335
ECO_DIFFTRU -0.425 0.149 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.9342176247 9.847000e-03 94.873325
VOY_FREQ -0.0000136088 6.144506e-06 -2.214791
PRED_ENV 0.0179682904 3.291389e-03 5.459181
ECO_DIFFTRUE -0.0080519710 7.807465e-03 -1.031317
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.57 -206.34 114.79 -229.57
full_model 7 -219.74 -206.64 116.87 -233.74 4.1689 1 0.04117 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "14" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.951 1 1.06 TRUE AW WL
2 0.902 163 1.56 TRUE BT AW
3 0.943 5 1.50 TRUE BT GH
4 0.989 304 1.52 FALSE BT HT
5 0.964 1 2.14 TRUE BT LB
6 0.974 67 3.16 FALSE BT MI
7 0.985 275 1.56 FALSE BT NO
8 0.955 60 1.57 TRUE BT RT
9 0.957 186 0.921 FALSE BT WL
10 0.956 126 2.25 TRUE BT ZB
11 1 16 1.29 FALSE CB PL
12 0.907 6 0.547 FALSE CB RC
13 0.913 1 1.07 TRUE GH WL
14 0.980 16 2.79 TRUE HN CB
15 0.993 1 2.81 TRUE HN HT
16 0.988 224 2.11 TRUE HT AW
17 0.969 8 2.09 TRUE HT GH
18 1.00 6 2.53 TRUE HT LB
19 0.994 209 2.94 FALSE HT MI
20 0.891 3684 0.0459 FALSE HT NO
21 0.997 1 2.74 TRUE HT PM
22 0.948 127 2.23 TRUE HT RT
23 0.905 13 1.55 FALSE HT WL
24 0.998 4 3.39 TRUE HT ZB
25 0.939 27 1.94 FALSE LB CB
26 0.942 1 1.50 TRUE LB MI
27 0.988 52 4.15 TRUE MI AW
28 0.998 92 2.94 FALSE MI NO
29 0.962 1 3.38 TRUE MI OK
30 0.991 1 4.18 TRUE MI RT
31 0.988 1 3.49 TRUE MI ZB
32 0.894 2 1.12 TRUE RT WL
33 0.971 117 2.06 TRUE SI AD
34 0.986 10 4.01 TRUE SI AW
35 0.973 3 3.18 TRUE SI BT
36 0.982 1 3.18 TRUE SI CB
37 0.996 7 3.93 TRUE SI GH
38 0.958 53 0.576 TRUE SI HN
39 0.997 53 2.81 TRUE SI HT
40 0.966 82 1.55 TRUE SI LB
41 0.998 65 2.80 TRUE SI NO
42 0.959 3 3.17 TRUE SI OK
43 0.998 14 3.86 TRUE SI PL
44 0.996 5 2.54 TRUE SI PM
45 0.967 19 2.94 TRUE SI RC
46 0.993 37 4.05 TRUE SI RT
47 0.986 2 3.51 TRUE SI ZB
48 0.996 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-219.7 -206.6 116.9 -233.7 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.30113 -0.47225 -0.00788 0.61214 1.87274
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001487 0.0122
PORT (Intercept) 0.0000000 0.0000
Residual 0.0003422 0.0185
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.342e-01 9.847e-03 94.873
VOY_FREQ -1.361e-05 6.145e-06 -2.215
PRED_ENV 1.797e-02 3.291e-03 5.459
ECO_DIFFTRUE -8.052e-03 7.807e-03 -1.031
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.432
PRED_ENV -0.668 0.335
ECO_DIFFTRU -0.425 0.149 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.9342176247 9.847000e-03 94.873325
VOY_FREQ -0.0000136088 6.144506e-06 -2.214791
PRED_ENV 0.0179682904 3.291389e-03 5.459181
ECO_DIFFTRUE -0.0080519710 7.807465e-03 -1.031317
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.57 -206.34 114.79 -229.57
full_model 7 -219.74 -206.64 116.87 -233.74 4.1689 1 0.04117 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "15" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 1 1.06 TRUE AW WL
2 0.847 163 1.56 TRUE BT AW
3 0.906 5 1.50 TRUE BT GH
4 0.980 304 1.52 FALSE BT HT
5 0.936 1 2.14 TRUE BT LB
6 0.933 67 3.16 FALSE BT MI
7 0.971 275 1.56 FALSE BT NO
8 0.927 60 1.57 TRUE BT RT
9 0.934 186 0.921 FALSE BT WL
10 0.931 126 2.25 TRUE BT ZB
11 0.998 16 1.29 FALSE CB PL
12 0.863 6 0.547 FALSE CB RC
13 0.879 1 1.07 TRUE GH WL
14 0.947 16 2.79 TRUE HN CB
15 0.988 1 2.81 TRUE HN HT
16 0.981 224 2.11 TRUE HT AW
17 0.951 8 2.09 TRUE HT GH
18 0.998 6 2.53 TRUE HT LB
19 0.987 209 2.94 FALSE HT MI
20 0.848 3684 0.0459 FALSE HT NO
21 0.995 1 2.74 TRUE HT PM
22 0.920 127 2.23 TRUE HT RT
23 0.865 13 1.55 FALSE HT WL
24 0.997 4 3.39 TRUE HT ZB
25 0.901 27 1.94 FALSE LB CB
26 0.907 1 1.50 TRUE LB MI
27 0.966 52 4.15 TRUE MI AW
28 0.990 92 2.94 FALSE MI NO
29 0.927 1 3.38 TRUE MI OK
30 0.976 1 4.18 TRUE MI RT
31 0.965 1 3.49 TRUE MI ZB
32 0.839 2 1.12 TRUE RT WL
33 0.945 117 2.06 TRUE SI AD
34 0.960 10 4.01 TRUE SI AW
35 0.947 3 3.18 TRUE SI BT
36 0.950 1 3.18 TRUE SI CB
37 0.986 7 3.93 TRUE SI GH
38 0.935 53 0.576 TRUE SI HN
39 0.994 53 2.81 TRUE SI HT
40 0.931 82 1.55 TRUE SI LB
41 0.995 65 2.80 TRUE SI NO
42 0.919 3 3.17 TRUE SI OK
43 0.997 14 3.86 TRUE SI PL
44 0.983 5 2.54 TRUE SI PM
45 0.928 19 2.94 TRUE SI RC
46 0.976 37 4.05 TRUE SI RT
47 0.952 2 3.51 TRUE SI ZB
48 0.987 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-177.9 -164.8 96.0 -191.9 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.29315 -0.46458 -0.09173 0.67273 2.09969
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0004305 0.02075
PORT (Intercept) 0.0000000 0.00000
Residual 0.0007803 0.02793
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.033e-01 1.524e-02 59.256
VOY_FREQ -1.883e-05 9.366e-06 -2.011
PRED_ENV 2.296e-02 5.046e-03 4.551
ECO_DIFFTRUE -1.269e-02 1.194e-02 -1.063
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.426
PRED_ENV -0.662 0.341
ECO_DIFFTRU -0.422 0.141 -0.251
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 9.033103e-01 1.524425e-02 59.255809
VOY_FREQ -1.883326e-05 9.365648e-06 -2.010887
PRED_ENV 2.296424e-02 5.045690e-03 4.551258
ECO_DIFFTRUE -1.268940e-02 1.194015e-02 -1.062751
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -176.43 -165.20 94.215 -188.43
full_model 7 -177.94 -164.84 95.970 -191.94 3.5104 1 0.06099 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "16" and formula index FIDX "2" in Summary Tables.Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 1 1.06 TRUE AW WL
2 0.847 163 1.56 TRUE BT AW
3 0.906 5 1.50 TRUE BT GH
4 0.980 304 1.52 FALSE BT HT
5 0.936 1 2.14 TRUE BT LB
6 0.933 67 3.16 FALSE BT MI
7 0.971 275 1.56 FALSE BT NO
8 0.927 60 1.57 TRUE BT RT
9 0.934 186 0.921 FALSE BT WL
10 0.931 126 2.25 TRUE BT ZB
11 0.998 16 1.29 FALSE CB PL
12 0.863 6 0.547 FALSE CB RC
13 0.879 1 1.07 TRUE GH WL
14 0.947 16 2.79 TRUE HN CB
15 0.988 1 2.81 TRUE HN HT
16 0.981 224 2.11 TRUE HT AW
17 0.951 8 2.09 TRUE HT GH
18 0.998 6 2.53 TRUE HT LB
19 0.987 209 2.94 FALSE HT MI
20 0.848 3684 0.0459 FALSE HT NO
21 0.995 1 2.74 TRUE HT PM
22 0.920 127 2.23 TRUE HT RT
23 0.865 13 1.55 FALSE HT WL
24 0.997 4 3.39 TRUE HT ZB
25 0.901 27 1.94 FALSE LB CB
26 0.907 1 1.50 TRUE LB MI
27 0.966 52 4.15 TRUE MI AW
28 0.990 92 2.94 FALSE MI NO
29 0.927 1 3.38 TRUE MI OK
30 0.976 1 4.18 TRUE MI RT
31 0.965 1 3.49 TRUE MI ZB
32 0.839 2 1.12 TRUE RT WL
33 0.945 117 2.06 TRUE SI AD
34 0.960 10 4.01 TRUE SI AW
35 0.947 3 3.18 TRUE SI BT
36 0.950 1 3.18 TRUE SI CB
37 0.986 7 3.93 TRUE SI GH
38 0.935 53 0.576 TRUE SI HN
39 0.994 53 2.81 TRUE SI HT
40 0.931 82 1.55 TRUE SI LB
41 0.995 65 2.80 TRUE SI NO
42 0.919 3 3.17 TRUE SI OK
43 0.997 14 3.86 TRUE SI PL
44 0.983 5 2.54 TRUE SI PM
45 0.928 19 2.94 TRUE SI RC
46 0.976 37 4.05 TRUE SI RT
47 0.952 2 3.51 TRUE SI ZB
48 0.987 2 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-177.9 -164.8 96.0 -191.9 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.29315 -0.46458 -0.09173 0.67273 2.09969
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0004305 0.02075
PORT (Intercept) 0.0000000 0.00000
Residual 0.0007803 0.02793
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 9.033e-01 1.524e-02 59.256
VOY_FREQ -1.883e-05 9.366e-06 -2.011
PRED_ENV 2.296e-02 5.046e-03 4.551
ECO_DIFFTRUE -1.269e-02 1.194e-02 -1.063
Correlation of Fixed Effects:
(Intr) VOY_FR PRED_E
VOY_FREQ -0.426
PRED_ENV -0.662 0.341
ECO_DIFFTRU -0.422 0.141 -0.251
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 9.033103e-01 1.524425e-02 59.255809
VOY_FREQ -1.883326e-05 9.365648e-06 -2.010887
PRED_ENV 2.296424e-02 5.045690e-03 4.551258
ECO_DIFFTRUE -1.268940e-02 1.194015e-02 -1.062751
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -176.43 -165.20 94.215 -188.43
full_model 7 -177.94 -164.84 95.970 -191.94 3.5104 1 0.06099 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "1" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.700 0. 1.06 TRUE AW WL
2 0.636 6.06e- 1 1.56 TRUE BT AW
3 0.695 7.58e- 2 1.50 TRUE BT GH
4 0.794 4.83e- 2 1.52 FALSE BT HT
5 0.785 8.92e- 4 2.14 TRUE BT LB
6 0.756 6.07e- 8 3.16 FALSE BT MI
7 0.794 1.83e- 1 1.56 FALSE BT NO
8 0.732 5.48e- 1 1.57 TRUE BT RT
9 0.723 9.60e- 1 0.921 FALSE BT WL
10 0.776 5.75e- 2 2.25 TRUE BT ZB
11 0.832 2.28e- 2 1.29 FALSE CB PL
12 0.683 2.07e- 4 0.547 FALSE CB RC
13 0.654 1.23e- 2 1.07 TRUE GH WL
14 0.739 1.00e-11 2.79 TRUE HN CB
15 0.829 0. 2.81 TRUE HN HT
16 0.774 8.75e- 6 2.11 TRUE HT AW
17 0.747 2.48e- 6 2.09 TRUE HT GH
18 0.885 3.08e- 4 2.53 TRUE HT LB
19 0.845 3.56e- 4 2.94 FALSE HT MI
20 0.628 1.00e+ 0 0.0459 FALSE HT NO
21 0.828 0. 2.74 TRUE HT PM
22 0.695 3.59e- 6 2.23 TRUE HT RT
23 0.639 7.49e- 4 1.55 FALSE HT WL
24 0.869 2.57e-10 3.39 TRUE HT ZB
25 0.738 8.05e- 6 1.94 FALSE LB CB
26 0.726 3.73e- 4 1.50 TRUE LB MI
27 0.748 0. 4.15 TRUE MI AW
28 0.864 3.18e- 4 2.94 FALSE MI NO
29 0.712 1.97e-11 3.38 TRUE MI OK
30 0.786 0. 4.18 TRUE MI RT
31 0.799 8.88e-16 3.49 TRUE MI ZB
32 0.603 5.46e- 2 1.12 TRUE RT WL
33 0.815 4.36e- 4 2.06 TRUE SI AD
34 0.740 5.55e-16 4.01 TRUE SI AW
35 0.748 4.13e- 9 3.18 TRUE SI BT
36 0.724 9.94e-12 3.18 TRUE SI CB
37 0.789 2.11e-15 3.93 TRUE SI GH
38 0.762 7.13e- 1 0.576 TRUE SI HN
39 0.836 6.99e- 4 2.81 TRUE SI HT
40 0.730 6.40e- 3 1.55 TRUE SI LB
41 0.853 4.30e- 3 2.80 TRUE SI NO
42 0.692 1.91e-10 3.17 TRUE SI OK
43 0.835 4.25e-14 3.86 TRUE SI PL
44 0.780 3.82e- 9 2.54 TRUE SI PM
45 0.691 1.30e- 8 2.94 TRUE SI RC
46 0.774 2.22e-16 4.05 TRUE SI RT
47 0.789 1.11e-15 3.51 TRUE SI ZB
48 0.817 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-132.0 -118.9 73.0 -146.0 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.89659 -0.60863 -0.08118 0.72351 2.01209
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 1.577e-03 3.971e-02
PORT (Intercept) 3.178e-19 5.637e-10
Residual 1.845e-03 4.295e-02
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.715394 0.026757 26.737
B_FON_NOECO -0.033056 0.034093 -0.970
PRED_ENV 0.028324 0.008562 3.308
ECO_DIFFTRUE -0.024783 0.018732 -1.323
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.544
PRED_ENV -0.698 0.491
ECO_DIFFTRU -0.391 0.107 -0.220
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.71539350 0.026756703 26.7369828
B_FON_NOECO -0.03305617 0.034092787 -0.9695942
PRED_ENV 0.02832402 0.008561583 3.3082693
ECO_DIFFTRUE -0.02478349 0.018732139 -1.3230462
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.05 -121.82 72.525 -145.05
full_model 7 -131.97 -118.88 72.988 -145.97 0.9261 1 0.3359
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "2" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.700 0. 1.06 TRUE AW WL
2 0.636 6.06e- 1 1.56 TRUE BT AW
3 0.695 7.58e- 2 1.50 TRUE BT GH
4 0.794 4.83e- 2 1.52 FALSE BT HT
5 0.785 8.92e- 4 2.14 TRUE BT LB
6 0.756 6.07e- 8 3.16 FALSE BT MI
7 0.794 1.83e- 1 1.56 FALSE BT NO
8 0.732 5.48e- 1 1.57 TRUE BT RT
9 0.723 9.60e- 1 0.921 FALSE BT WL
10 0.776 5.75e- 2 2.25 TRUE BT ZB
11 0.832 2.28e- 2 1.29 FALSE CB PL
12 0.683 2.07e- 4 0.547 FALSE CB RC
13 0.654 1.23e- 2 1.07 TRUE GH WL
14 0.739 1.00e-11 2.79 TRUE HN CB
15 0.829 0. 2.81 TRUE HN HT
16 0.774 8.75e- 6 2.11 TRUE HT AW
17 0.747 2.48e- 6 2.09 TRUE HT GH
18 0.885 3.08e- 4 2.53 TRUE HT LB
19 0.845 3.56e- 4 2.94 FALSE HT MI
20 0.628 1.00e+ 0 0.0459 FALSE HT NO
21 0.828 0. 2.74 TRUE HT PM
22 0.695 3.59e- 6 2.23 TRUE HT RT
23 0.639 7.49e- 4 1.55 FALSE HT WL
24 0.869 2.57e-10 3.39 TRUE HT ZB
25 0.738 8.05e- 6 1.94 FALSE LB CB
26 0.726 3.73e- 4 1.50 TRUE LB MI
27 0.748 0. 4.15 TRUE MI AW
28 0.864 3.18e- 4 2.94 FALSE MI NO
29 0.712 1.97e-11 3.38 TRUE MI OK
30 0.786 0. 4.18 TRUE MI RT
31 0.799 8.88e-16 3.49 TRUE MI ZB
32 0.603 5.46e- 2 1.12 TRUE RT WL
33 0.815 4.36e- 4 2.06 TRUE SI AD
34 0.740 5.55e-16 4.01 TRUE SI AW
35 0.748 4.13e- 9 3.18 TRUE SI BT
36 0.724 9.94e-12 3.18 TRUE SI CB
37 0.789 2.11e-15 3.93 TRUE SI GH
38 0.762 7.13e- 1 0.576 TRUE SI HN
39 0.836 6.99e- 4 2.81 TRUE SI HT
40 0.730 6.40e- 3 1.55 TRUE SI LB
41 0.853 4.30e- 3 2.80 TRUE SI NO
42 0.692 1.91e-10 3.17 TRUE SI OK
43 0.835 4.25e-14 3.86 TRUE SI PL
44 0.780 3.82e- 9 2.54 TRUE SI PM
45 0.691 1.30e- 8 2.94 TRUE SI RC
46 0.774 2.22e-16 4.05 TRUE SI RT
47 0.789 1.11e-15 3.51 TRUE SI ZB
48 0.817 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-132.0 -118.9 73.0 -146.0 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.89659 -0.60863 -0.08118 0.72351 2.01209
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001577 0.03971
PORT (Intercept) 0.000000 0.00000
Residual 0.001845 0.04295
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.715394 0.026757 26.737
B_FON_NOECO -0.033056 0.034093 -0.970
PRED_ENV 0.028324 0.008562 3.308
ECO_DIFFTRUE -0.024783 0.018732 -1.323
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.544
PRED_ENV -0.698 0.491
ECO_DIFFTRU -0.391 0.107 -0.220
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.71539351 0.026756703 26.7369825
B_FON_NOECO -0.03305618 0.034092789 -0.9695945
PRED_ENV 0.02832402 0.008561584 3.3082689
ECO_DIFFTRUE -0.02478349 0.018732139 -1.3230462
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.05 -121.82 72.525 -145.05
full_model 7 -131.97 -118.88 72.988 -145.97 0.9261 1 0.3359
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "3" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.708 0. 1.06 TRUE AW WL
2 0.634 6.06e- 1 1.56 TRUE BT AW
3 0.708 7.58e- 2 1.50 TRUE BT GH
4 0.800 4.83e- 2 1.52 FALSE BT HT
5 0.791 8.92e- 4 2.14 TRUE BT LB
6 0.751 6.07e- 8 3.16 FALSE BT MI
7 0.802 1.83e- 1 1.56 FALSE BT NO
8 0.732 5.48e- 1 1.57 TRUE BT RT
9 0.740 9.60e- 1 0.921 FALSE BT WL
10 0.786 5.75e- 2 2.25 TRUE BT ZB
11 0.831 2.28e- 2 1.29 FALSE CB PL
12 0.688 2.07e- 4 0.547 FALSE CB RC
13 0.671 1.23e- 2 1.07 TRUE GH WL
14 0.736 1.00e-11 2.79 TRUE HN CB
15 0.830 0. 2.81 TRUE HN HT
16 0.781 8.75e- 6 2.11 TRUE HT AW
17 0.753 2.48e- 6 2.09 TRUE HT GH
18 0.892 3.08e- 4 2.53 TRUE HT LB
19 0.851 3.56e- 4 2.94 FALSE HT MI
20 0.635 1.00e+ 0 0.0459 FALSE HT NO
21 0.824 0. 2.74 TRUE HT PM
22 0.700 3.59e- 6 2.23 TRUE HT RT
23 0.644 7.49e- 4 1.55 FALSE HT WL
24 0.879 2.57e-10 3.39 TRUE HT ZB
25 0.730 8.05e- 6 1.94 FALSE LB CB
26 0.726 3.73e- 4 1.50 TRUE LB MI
27 0.747 0. 4.15 TRUE MI AW
28 0.870 3.18e- 4 2.94 FALSE MI NO
29 0.715 1.97e-11 3.38 TRUE MI OK
30 0.789 0. 4.18 TRUE MI RT
31 0.802 8.88e-16 3.49 TRUE MI ZB
32 0.607 5.46e- 2 1.12 TRUE RT WL
33 0.815 4.36e- 4 2.06 TRUE SI AD
34 0.737 5.55e-16 4.01 TRUE SI AW
35 0.747 4.13e- 9 3.18 TRUE SI BT
36 0.724 9.94e-12 3.18 TRUE SI CB
37 0.790 2.11e-15 3.93 TRUE SI GH
38 0.768 7.13e- 1 0.576 TRUE SI HN
39 0.837 6.99e- 4 2.81 TRUE SI HT
40 0.732 6.40e- 3 1.55 TRUE SI LB
41 0.851 4.30e- 3 2.80 TRUE SI NO
42 0.698 1.91e-10 3.17 TRUE SI OK
43 0.836 4.25e-14 3.86 TRUE SI PL
44 0.780 3.82e- 9 2.54 TRUE SI PM
45 0.702 1.30e- 8 2.94 TRUE SI RC
46 0.774 2.22e-16 4.05 TRUE SI RT
47 0.786 1.11e-15 3.51 TRUE SI ZB
48 0.821 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-130.3 -117.2 72.2 -144.3 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.84400 -0.60695 -0.09389 0.62813 2.04404
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001445 0.03802
PORT (Intercept) 0.000000 0.00000
Residual 0.001982 0.04452
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.72054 0.02724 26.451
B_FON_NOECO -0.03048 0.03512 -0.868
PRED_ENV 0.02716 0.00881 3.083
ECO_DIFFTRUE -0.02450 0.01924 -1.273
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.549
PRED_ENV -0.704 0.492
ECO_DIFFTRU -0.390 0.103 -0.224
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.72054278 0.027240762 26.4509043
B_FON_NOECO -0.03048395 0.035115769 -0.8680985
PRED_ENV 0.02716230 0.008809728 3.0832167
ECO_DIFFTRUE -0.02450225 0.019242120 -1.2733656
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -131.56 -120.33 71.780 -143.56
full_model 7 -130.31 -117.21 72.153 -144.31 0.7453 1 0.388
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "4" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.708 0. 1.06 TRUE AW WL
2 0.634 6.06e- 1 1.56 TRUE BT AW
3 0.708 7.58e- 2 1.50 TRUE BT GH
4 0.800 4.83e- 2 1.52 FALSE BT HT
5 0.791 8.92e- 4 2.14 TRUE BT LB
6 0.751 6.07e- 8 3.16 FALSE BT MI
7 0.802 1.83e- 1 1.56 FALSE BT NO
8 0.732 5.48e- 1 1.57 TRUE BT RT
9 0.740 9.60e- 1 0.921 FALSE BT WL
10 0.786 5.75e- 2 2.25 TRUE BT ZB
11 0.831 2.28e- 2 1.29 FALSE CB PL
12 0.688 2.07e- 4 0.547 FALSE CB RC
13 0.671 1.23e- 2 1.07 TRUE GH WL
14 0.736 1.00e-11 2.79 TRUE HN CB
15 0.830 0. 2.81 TRUE HN HT
16 0.781 8.75e- 6 2.11 TRUE HT AW
17 0.753 2.48e- 6 2.09 TRUE HT GH
18 0.892 3.08e- 4 2.53 TRUE HT LB
19 0.851 3.56e- 4 2.94 FALSE HT MI
20 0.635 1.00e+ 0 0.0459 FALSE HT NO
21 0.824 0. 2.74 TRUE HT PM
22 0.700 3.59e- 6 2.23 TRUE HT RT
23 0.644 7.49e- 4 1.55 FALSE HT WL
24 0.879 2.57e-10 3.39 TRUE HT ZB
25 0.730 8.05e- 6 1.94 FALSE LB CB
26 0.726 3.73e- 4 1.50 TRUE LB MI
27 0.747 0. 4.15 TRUE MI AW
28 0.870 3.18e- 4 2.94 FALSE MI NO
29 0.715 1.97e-11 3.38 TRUE MI OK
30 0.789 0. 4.18 TRUE MI RT
31 0.802 8.88e-16 3.49 TRUE MI ZB
32 0.607 5.46e- 2 1.12 TRUE RT WL
33 0.815 4.36e- 4 2.06 TRUE SI AD
34 0.737 5.55e-16 4.01 TRUE SI AW
35 0.747 4.13e- 9 3.18 TRUE SI BT
36 0.724 9.94e-12 3.18 TRUE SI CB
37 0.790 2.11e-15 3.93 TRUE SI GH
38 0.768 7.13e- 1 0.576 TRUE SI HN
39 0.837 6.99e- 4 2.81 TRUE SI HT
40 0.732 6.40e- 3 1.55 TRUE SI LB
41 0.851 4.30e- 3 2.80 TRUE SI NO
42 0.698 1.91e-10 3.17 TRUE SI OK
43 0.836 4.25e-14 3.86 TRUE SI PL
44 0.780 3.82e- 9 2.54 TRUE SI PM
45 0.702 1.30e- 8 2.94 TRUE SI RC
46 0.774 2.22e-16 4.05 TRUE SI RT
47 0.786 1.11e-15 3.51 TRUE SI ZB
48 0.821 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-130.3 -117.2 72.2 -144.3 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.84400 -0.60695 -0.09389 0.62813 2.04404
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001445 0.03802
PORT (Intercept) 0.000000 0.00000
Residual 0.001982 0.04452
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.72054 0.02724 26.451
B_FON_NOECO -0.03048 0.03512 -0.868
PRED_ENV 0.02716 0.00881 3.083
ECO_DIFFTRUE -0.02450 0.01924 -1.273
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.549
PRED_ENV -0.704 0.492
ECO_DIFFTRU -0.390 0.103 -0.224
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.72054278 0.027240762 26.4509043
B_FON_NOECO -0.03048395 0.035115769 -0.8680985
PRED_ENV 0.02716230 0.008809728 3.0832167
ECO_DIFFTRUE -0.02450225 0.019242120 -1.2733656
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -131.56 -120.33 71.780 -143.56
full_model 7 -130.31 -117.21 72.153 -144.31 0.7453 1 0.388
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "5" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.950 0. 1.06 TRUE AW WL
2 0.902 6.06e- 1 1.56 TRUE BT AW
3 0.942 7.58e- 2 1.50 TRUE BT GH
4 0.989 4.83e- 2 1.52 FALSE BT HT
5 0.964 8.92e- 4 2.14 TRUE BT LB
6 0.974 6.07e- 8 3.16 FALSE BT MI
7 0.985 1.83e- 1 1.56 FALSE BT NO
8 0.955 5.48e- 1 1.57 TRUE BT RT
9 0.958 9.60e- 1 0.921 FALSE BT WL
10 0.955 5.75e- 2 2.25 TRUE BT ZB
11 1 2.28e- 2 1.29 FALSE CB PL
12 0.908 2.07e- 4 0.547 FALSE CB RC
13 0.913 1.23e- 2 1.07 TRUE GH WL
14 0.980 1.00e-11 2.79 TRUE HN CB
15 0.993 0. 2.81 TRUE HN HT
16 0.988 8.75e- 6 2.11 TRUE HT AW
17 0.969 2.48e- 6 2.09 TRUE HT GH
18 1.00 3.08e- 4 2.53 TRUE HT LB
19 0.994 3.56e- 4 2.94 FALSE HT MI
20 0.892 1.00e+ 0 0.0459 FALSE HT NO
21 0.997 0. 2.74 TRUE HT PM
22 0.948 3.59e- 6 2.23 TRUE HT RT
23 0.906 7.49e- 4 1.55 FALSE HT WL
24 0.999 2.57e-10 3.39 TRUE HT ZB
25 0.940 8.05e- 6 1.94 FALSE LB CB
26 0.943 3.73e- 4 1.50 TRUE LB MI
27 0.988 0. 4.15 TRUE MI AW
28 0.998 3.18e- 4 2.94 FALSE MI NO
29 0.963 1.97e-11 3.38 TRUE MI OK
30 0.991 0. 4.18 TRUE MI RT
31 0.988 8.88e-16 3.49 TRUE MI ZB
32 0.894 5.46e- 2 1.12 TRUE RT WL
33 0.971 4.36e- 4 2.06 TRUE SI AD
34 0.985 5.55e-16 4.01 TRUE SI AW
35 0.973 4.13e- 9 3.18 TRUE SI BT
36 0.981 9.94e-12 3.18 TRUE SI CB
37 0.995 2.11e-15 3.93 TRUE SI GH
38 0.959 7.13e- 1 0.576 TRUE SI HN
39 0.997 6.99e- 4 2.81 TRUE SI HT
40 0.967 6.40e- 3 1.55 TRUE SI LB
41 0.997 4.30e- 3 2.80 TRUE SI NO
42 0.958 1.91e-10 3.17 TRUE SI OK
43 0.998 4.25e-14 3.86 TRUE SI PL
44 0.996 3.82e- 9 2.54 TRUE SI PM
45 0.965 1.30e- 8 2.94 TRUE SI RC
46 0.992 2.22e-16 4.05 TRUE SI RT
47 0.984 1.11e-15 3.51 TRUE SI ZB
48 0.996 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-216.2 -203.1 115.1 -230.2 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.0393 -0.4659 -0.1089 0.5976 1.9272
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 9.362e-05 0.009676
PORT (Intercept) 0.000e+00 0.000000
Residual 4.079e-04 0.020197
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.929066 0.011060 84.004
B_FON_NOECO -0.008958 0.015226 -0.588
PRED_ENV 0.019068 0.003771 5.057
ECO_DIFFTRUE -0.006355 0.008160 -0.779
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.570
PRED_ENV -0.731 0.495
ECO_DIFFTRU -0.375 0.081 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.929065909 0.011059769 84.0040949
B_FON_NOECO -0.008957681 0.015225644 -0.5883285
PRED_ENV 0.019067655 0.003770907 5.0565167
ECO_DIFFTRUE -0.006354684 0.008160338 -0.7787280
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.82 -206.59 114.91 -229.82
full_model 7 -216.16 -203.07 115.08 -230.16 0.3449 1 0.557
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "6" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.950 0. 1.06 TRUE AW WL
2 0.902 6.06e- 1 1.56 TRUE BT AW
3 0.942 7.58e- 2 1.50 TRUE BT GH
4 0.989 4.83e- 2 1.52 FALSE BT HT
5 0.964 8.92e- 4 2.14 TRUE BT LB
6 0.974 6.07e- 8 3.16 FALSE BT MI
7 0.985 1.83e- 1 1.56 FALSE BT NO
8 0.955 5.48e- 1 1.57 TRUE BT RT
9 0.958 9.60e- 1 0.921 FALSE BT WL
10 0.955 5.75e- 2 2.25 TRUE BT ZB
11 1 2.28e- 2 1.29 FALSE CB PL
12 0.908 2.07e- 4 0.547 FALSE CB RC
13 0.913 1.23e- 2 1.07 TRUE GH WL
14 0.980 1.00e-11 2.79 TRUE HN CB
15 0.993 0. 2.81 TRUE HN HT
16 0.988 8.75e- 6 2.11 TRUE HT AW
17 0.969 2.48e- 6 2.09 TRUE HT GH
18 1.00 3.08e- 4 2.53 TRUE HT LB
19 0.994 3.56e- 4 2.94 FALSE HT MI
20 0.892 1.00e+ 0 0.0459 FALSE HT NO
21 0.997 0. 2.74 TRUE HT PM
22 0.948 3.59e- 6 2.23 TRUE HT RT
23 0.906 7.49e- 4 1.55 FALSE HT WL
24 0.999 2.57e-10 3.39 TRUE HT ZB
25 0.940 8.05e- 6 1.94 FALSE LB CB
26 0.943 3.73e- 4 1.50 TRUE LB MI
27 0.988 0. 4.15 TRUE MI AW
28 0.998 3.18e- 4 2.94 FALSE MI NO
29 0.963 1.97e-11 3.38 TRUE MI OK
30 0.991 0. 4.18 TRUE MI RT
31 0.988 8.88e-16 3.49 TRUE MI ZB
32 0.894 5.46e- 2 1.12 TRUE RT WL
33 0.971 4.36e- 4 2.06 TRUE SI AD
34 0.985 5.55e-16 4.01 TRUE SI AW
35 0.973 4.13e- 9 3.18 TRUE SI BT
36 0.981 9.94e-12 3.18 TRUE SI CB
37 0.995 2.11e-15 3.93 TRUE SI GH
38 0.959 7.13e- 1 0.576 TRUE SI HN
39 0.997 6.99e- 4 2.81 TRUE SI HT
40 0.967 6.40e- 3 1.55 TRUE SI LB
41 0.997 4.30e- 3 2.80 TRUE SI NO
42 0.958 1.91e-10 3.17 TRUE SI OK
43 0.998 4.25e-14 3.86 TRUE SI PL
44 0.996 3.82e- 9 2.54 TRUE SI PM
45 0.965 1.30e- 8 2.94 TRUE SI RC
46 0.992 2.22e-16 4.05 TRUE SI RT
47 0.984 1.11e-15 3.51 TRUE SI ZB
48 0.996 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-216.2 -203.1 115.1 -230.2 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.0393 -0.4659 -0.1089 0.5976 1.9272
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 9.362e-05 0.009676
PORT (Intercept) 0.000e+00 0.000000
Residual 4.079e-04 0.020197
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.929066 0.011060 84.004
B_FON_NOECO -0.008958 0.015226 -0.588
PRED_ENV 0.019068 0.003771 5.057
ECO_DIFFTRUE -0.006355 0.008160 -0.779
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.570
PRED_ENV -0.731 0.495
ECO_DIFFTRU -0.375 0.081 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.929065909 0.011059769 84.0040949
B_FON_NOECO -0.008957681 0.015225644 -0.5883285
PRED_ENV 0.019067655 0.003770907 5.0565167
ECO_DIFFTRUE -0.006354684 0.008160338 -0.7787280
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.82 -206.59 114.91 -229.82
full_model 7 -216.16 -203.07 115.08 -230.16 0.3449 1 0.557
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "7" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 0. 1.06 TRUE AW WL
2 0.848 6.06e- 1 1.56 TRUE BT AW
3 0.907 7.58e- 2 1.50 TRUE BT GH
4 0.980 4.83e- 2 1.52 FALSE BT HT
5 0.938 8.92e- 4 2.14 TRUE BT LB
6 0.934 6.07e- 8 3.16 FALSE BT MI
7 0.971 1.83e- 1 1.56 FALSE BT NO
8 0.928 5.48e- 1 1.57 TRUE BT RT
9 0.935 9.60e- 1 0.921 FALSE BT WL
10 0.932 5.75e- 2 2.25 TRUE BT ZB
11 0.998 2.28e- 2 1.29 FALSE CB PL
12 0.863 2.07e- 4 0.547 FALSE CB RC
13 0.879 1.23e- 2 1.07 TRUE GH WL
14 0.948 1.00e-11 2.79 TRUE HN CB
15 0.988 0. 2.81 TRUE HN HT
16 0.981 8.75e- 6 2.11 TRUE HT AW
17 0.951 2.48e- 6 2.09 TRUE HT GH
18 0.998 3.08e- 4 2.53 TRUE HT LB
19 0.987 3.56e- 4 2.94 FALSE HT MI
20 0.848 1.00e+ 0 0.0459 FALSE HT NO
21 0.995 0. 2.74 TRUE HT PM
22 0.921 3.59e- 6 2.23 TRUE HT RT
23 0.866 7.49e- 4 1.55 FALSE HT WL
24 0.997 2.57e-10 3.39 TRUE HT ZB
25 0.902 8.05e- 6 1.94 FALSE LB CB
26 0.908 3.73e- 4 1.50 TRUE LB MI
27 0.967 0. 4.15 TRUE MI AW
28 0.990 3.18e- 4 2.94 FALSE MI NO
29 0.928 1.97e-11 3.38 TRUE MI OK
30 0.977 0. 4.18 TRUE MI RT
31 0.966 8.88e-16 3.49 TRUE MI ZB
32 0.840 5.46e- 2 1.12 TRUE RT WL
33 0.946 4.36e- 4 2.06 TRUE SI AD
34 0.960 5.55e-16 4.01 TRUE SI AW
35 0.947 4.13e- 9 3.18 TRUE SI BT
36 0.950 9.94e-12 3.18 TRUE SI CB
37 0.986 2.11e-15 3.93 TRUE SI GH
38 0.935 7.13e- 1 0.576 TRUE SI HN
39 0.993 6.99e- 4 2.81 TRUE SI HT
40 0.932 6.40e- 3 1.55 TRUE SI LB
41 0.994 4.30e- 3 2.80 TRUE SI NO
42 0.922 1.91e-10 3.17 TRUE SI OK
43 0.996 4.25e-14 3.86 TRUE SI PL
44 0.983 3.82e- 9 2.54 TRUE SI PM
45 0.930 1.30e- 8 2.94 TRUE SI RC
46 0.976 2.22e-16 4.05 TRUE SI RT
47 0.952 1.11e-15 3.51 TRUE SI ZB
48 0.987 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-175.9 -162.8 94.9 -189.9 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.9400 -0.5252 -0.1045 0.7998 1.7730
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0002932 0.01712
PORT (Intercept) 0.0000000 0.00000
Residual 0.0008959 0.02993
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.897203 0.016894 53.107
B_FON_NOECO -0.014758 0.022861 -0.646
PRED_ENV 0.024307 0.005689 4.272
ECO_DIFFTRUE -0.009903 0.012336 -0.803
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.565
PRED_ENV -0.726 0.495
ECO_DIFFTRU -0.381 0.087 -0.244
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.897203218 0.01689411 53.1074644
B_FON_NOECO -0.014757655 0.02286148 -0.6455249
PRED_ENV 0.024306591 0.00568914 4.2724546
ECO_DIFFTRUE -0.009902955 0.01233641 -0.8027422
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -177.49 -166.26 94.743 -189.49
full_model 7 -175.90 -162.80 94.950 -189.90 0.4134 1 0.5203
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "8" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 0. 1.06 TRUE AW WL
2 0.848 6.06e- 1 1.56 TRUE BT AW
3 0.907 7.58e- 2 1.50 TRUE BT GH
4 0.980 4.83e- 2 1.52 FALSE BT HT
5 0.938 8.92e- 4 2.14 TRUE BT LB
6 0.934 6.07e- 8 3.16 FALSE BT MI
7 0.971 1.83e- 1 1.56 FALSE BT NO
8 0.928 5.48e- 1 1.57 TRUE BT RT
9 0.935 9.60e- 1 0.921 FALSE BT WL
10 0.932 5.75e- 2 2.25 TRUE BT ZB
11 0.998 2.28e- 2 1.29 FALSE CB PL
12 0.863 2.07e- 4 0.547 FALSE CB RC
13 0.879 1.23e- 2 1.07 TRUE GH WL
14 0.948 1.00e-11 2.79 TRUE HN CB
15 0.988 0. 2.81 TRUE HN HT
16 0.981 8.75e- 6 2.11 TRUE HT AW
17 0.951 2.48e- 6 2.09 TRUE HT GH
18 0.998 3.08e- 4 2.53 TRUE HT LB
19 0.987 3.56e- 4 2.94 FALSE HT MI
20 0.848 1.00e+ 0 0.0459 FALSE HT NO
21 0.995 0. 2.74 TRUE HT PM
22 0.921 3.59e- 6 2.23 TRUE HT RT
23 0.866 7.49e- 4 1.55 FALSE HT WL
24 0.997 2.57e-10 3.39 TRUE HT ZB
25 0.902 8.05e- 6 1.94 FALSE LB CB
26 0.908 3.73e- 4 1.50 TRUE LB MI
27 0.967 0. 4.15 TRUE MI AW
28 0.990 3.18e- 4 2.94 FALSE MI NO
29 0.928 1.97e-11 3.38 TRUE MI OK
30 0.977 0. 4.18 TRUE MI RT
31 0.966 8.88e-16 3.49 TRUE MI ZB
32 0.840 5.46e- 2 1.12 TRUE RT WL
33 0.946 4.36e- 4 2.06 TRUE SI AD
34 0.960 5.55e-16 4.01 TRUE SI AW
35 0.947 4.13e- 9 3.18 TRUE SI BT
36 0.950 9.94e-12 3.18 TRUE SI CB
37 0.986 2.11e-15 3.93 TRUE SI GH
38 0.935 7.13e- 1 0.576 TRUE SI HN
39 0.993 6.99e- 4 2.81 TRUE SI HT
40 0.932 6.40e- 3 1.55 TRUE SI LB
41 0.994 4.30e- 3 2.80 TRUE SI NO
42 0.922 1.91e-10 3.17 TRUE SI OK
43 0.996 4.25e-14 3.86 TRUE SI PL
44 0.983 3.82e- 9 2.54 TRUE SI PM
45 0.930 1.30e- 8 2.94 TRUE SI RC
46 0.976 2.22e-16 4.05 TRUE SI RT
47 0.952 1.11e-15 3.51 TRUE SI ZB
48 0.987 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-175.9 -162.8 94.9 -189.9 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.9400 -0.5252 -0.1045 0.7998 1.7730
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0002932 0.01712
PORT (Intercept) 0.0000000 0.00000
Residual 0.0008959 0.02993
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.897203 0.016894 53.107
B_FON_NOECO -0.014758 0.022861 -0.646
PRED_ENV 0.024307 0.005689 4.272
ECO_DIFFTRUE -0.009903 0.012336 -0.803
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.565
PRED_ENV -0.726 0.495
ECO_DIFFTRU -0.381 0.087 -0.244
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.897203218 0.01689411 53.1074644
B_FON_NOECO -0.014757655 0.02286148 -0.6455249
PRED_ENV 0.024306591 0.00568914 4.2724546
ECO_DIFFTRUE -0.009902955 0.01233641 -0.8027422
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -177.49 -166.26 94.743 -189.49
full_model 7 -175.90 -162.80 94.950 -189.90 0.4134 1 0.5203
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "9" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.702 0. 1.06 TRUE AW WL
2 0.634 6.06e- 1 1.56 TRUE BT AW
3 0.713 7.58e- 2 1.50 TRUE BT GH
4 0.800 4.83e- 2 1.52 FALSE BT HT
5 0.782 8.92e- 4 2.14 TRUE BT LB
6 0.760 6.07e- 8 3.16 FALSE BT MI
7 0.801 1.83e- 1 1.56 FALSE BT NO
8 0.728 5.48e- 1 1.57 TRUE BT RT
9 0.728 9.60e- 1 0.921 FALSE BT WL
10 0.764 5.75e- 2 2.25 TRUE BT ZB
11 0.836 2.28e- 2 1.29 FALSE CB PL
12 0.683 2.07e- 4 0.547 FALSE CB RC
13 0.673 1.23e- 2 1.07 TRUE GH WL
14 0.741 1.00e-11 2.79 TRUE HN CB
15 0.840 0. 2.81 TRUE HN HT
16 0.779 8.75e- 6 2.11 TRUE HT AW
17 0.765 2.48e- 6 2.09 TRUE HT GH
18 0.889 3.08e- 4 2.53 TRUE HT LB
19 0.849 3.56e- 4 2.94 FALSE HT MI
20 0.632 1.00e+ 0 0.0459 FALSE HT NO
21 0.838 0. 2.74 TRUE HT PM
22 0.701 3.59e- 6 2.23 TRUE HT RT
23 0.653 7.49e- 4 1.55 FALSE HT WL
24 0.874 2.57e-10 3.39 TRUE HT ZB
25 0.735 8.05e- 6 1.94 FALSE LB CB
26 0.721 3.73e- 4 1.50 TRUE LB MI
27 0.745 0. 4.15 TRUE MI AW
28 0.869 3.18e- 4 2.94 FALSE MI NO
29 0.719 1.97e-11 3.38 TRUE MI OK
30 0.785 0. 4.18 TRUE MI RT
31 0.790 8.88e-16 3.49 TRUE MI ZB
32 0.603 5.46e- 2 1.12 TRUE RT WL
33 0.796 4.36e- 4 2.06 TRUE SI AD
34 0.741 5.55e-16 4.01 TRUE SI AW
35 0.743 4.13e- 9 3.18 TRUE SI BT
36 0.737 9.94e-12 3.18 TRUE SI CB
37 0.797 2.11e-15 3.93 TRUE SI GH
38 0.761 7.13e- 1 0.576 TRUE SI HN
39 0.842 6.99e- 4 2.81 TRUE SI HT
40 0.727 6.40e- 3 1.55 TRUE SI LB
41 0.858 4.30e- 3 2.80 TRUE SI NO
42 0.706 1.91e-10 3.17 TRUE SI OK
43 0.841 4.25e-14 3.86 TRUE SI PL
44 0.781 3.82e- 9 2.54 TRUE SI PM
45 0.699 1.30e- 8 2.94 TRUE SI RC
46 0.777 2.22e-16 4.05 TRUE SI RT
47 0.777 1.11e-15 3.51 TRUE SI ZB
48 0.813 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-132.2 -119.1 73.1 -146.2 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.7977 -0.6370 -0.1495 0.6307 2.1004
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 1.370e-03 3.701e-02
PORT (Intercept) 1.251e-12 1.118e-06
Residual 1.913e-03 4.374e-02
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.718385 0.026708 26.898
B_FON_NOECO -0.036111 0.034473 -1.048
PRED_ENV 0.028609 0.008647 3.308
ECO_DIFFTRUE -0.026066 0.018884 -1.380
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.549
PRED_ENV -0.705 0.492
ECO_DIFFTRU -0.390 0.103 -0.224
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.71838513 0.026708008 26.897743
B_FON_NOECO -0.03611096 0.034473170 -1.047509
PRED_ENV 0.02860925 0.008647411 3.308418
ECO_DIFFTRUE -0.02606615 0.018883943 -1.380334
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.12 -121.90 72.563 -145.12
full_model 7 -132.21 -119.11 73.103 -146.21 1.0802 1 0.2987
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "10" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.702 0. 1.06 TRUE AW WL
2 0.634 6.06e- 1 1.56 TRUE BT AW
3 0.713 7.58e- 2 1.50 TRUE BT GH
4 0.800 4.83e- 2 1.52 FALSE BT HT
5 0.782 8.92e- 4 2.14 TRUE BT LB
6 0.760 6.07e- 8 3.16 FALSE BT MI
7 0.801 1.83e- 1 1.56 FALSE BT NO
8 0.728 5.48e- 1 1.57 TRUE BT RT
9 0.728 9.60e- 1 0.921 FALSE BT WL
10 0.764 5.75e- 2 2.25 TRUE BT ZB
11 0.836 2.28e- 2 1.29 FALSE CB PL
12 0.683 2.07e- 4 0.547 FALSE CB RC
13 0.673 1.23e- 2 1.07 TRUE GH WL
14 0.741 1.00e-11 2.79 TRUE HN CB
15 0.840 0. 2.81 TRUE HN HT
16 0.779 8.75e- 6 2.11 TRUE HT AW
17 0.765 2.48e- 6 2.09 TRUE HT GH
18 0.889 3.08e- 4 2.53 TRUE HT LB
19 0.849 3.56e- 4 2.94 FALSE HT MI
20 0.632 1.00e+ 0 0.0459 FALSE HT NO
21 0.838 0. 2.74 TRUE HT PM
22 0.701 3.59e- 6 2.23 TRUE HT RT
23 0.653 7.49e- 4 1.55 FALSE HT WL
24 0.874 2.57e-10 3.39 TRUE HT ZB
25 0.735 8.05e- 6 1.94 FALSE LB CB
26 0.721 3.73e- 4 1.50 TRUE LB MI
27 0.745 0. 4.15 TRUE MI AW
28 0.869 3.18e- 4 2.94 FALSE MI NO
29 0.719 1.97e-11 3.38 TRUE MI OK
30 0.785 0. 4.18 TRUE MI RT
31 0.790 8.88e-16 3.49 TRUE MI ZB
32 0.603 5.46e- 2 1.12 TRUE RT WL
33 0.796 4.36e- 4 2.06 TRUE SI AD
34 0.741 5.55e-16 4.01 TRUE SI AW
35 0.743 4.13e- 9 3.18 TRUE SI BT
36 0.737 9.94e-12 3.18 TRUE SI CB
37 0.797 2.11e-15 3.93 TRUE SI GH
38 0.761 7.13e- 1 0.576 TRUE SI HN
39 0.842 6.99e- 4 2.81 TRUE SI HT
40 0.727 6.40e- 3 1.55 TRUE SI LB
41 0.858 4.30e- 3 2.80 TRUE SI NO
42 0.706 1.91e-10 3.17 TRUE SI OK
43 0.841 4.25e-14 3.86 TRUE SI PL
44 0.781 3.82e- 9 2.54 TRUE SI PM
45 0.699 1.30e- 8 2.94 TRUE SI RC
46 0.777 2.22e-16 4.05 TRUE SI RT
47 0.777 1.11e-15 3.51 TRUE SI ZB
48 0.813 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-132.2 -119.1 73.1 -146.2 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.7977 -0.6370 -0.1495 0.6307 2.1004
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 1.370e-03 3.701e-02
PORT (Intercept) 1.251e-12 1.118e-06
Residual 1.913e-03 4.374e-02
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.718385 0.026708 26.898
B_FON_NOECO -0.036111 0.034473 -1.048
PRED_ENV 0.028609 0.008647 3.308
ECO_DIFFTRUE -0.026066 0.018884 -1.380
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.549
PRED_ENV -0.705 0.492
ECO_DIFFTRU -0.390 0.103 -0.224
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.71838513 0.026708008 26.897743
B_FON_NOECO -0.03611096 0.034473170 -1.047509
PRED_ENV 0.02860925 0.008647411 3.308418
ECO_DIFFTRUE -0.02606615 0.018883943 -1.380334
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.12 -121.90 72.563 -145.12
full_model 7 -132.21 -119.11 73.103 -146.21 1.0802 1 0.2987
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "11" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.716 0. 1.06 TRUE AW WL
2 0.625 6.06e- 1 1.56 TRUE BT AW
3 0.705 7.58e- 2 1.50 TRUE BT GH
4 0.800 4.83e- 2 1.52 FALSE BT HT
5 0.777 8.92e- 4 2.14 TRUE BT LB
6 0.750 6.07e- 8 3.16 FALSE BT MI
7 0.795 1.83e- 1 1.56 FALSE BT NO
8 0.735 5.48e- 1 1.57 TRUE BT RT
9 0.733 9.60e- 1 0.921 FALSE BT WL
10 0.776 5.75e- 2 2.25 TRUE BT ZB
11 0.840 2.28e- 2 1.29 FALSE CB PL
12 0.675 2.07e- 4 0.547 FALSE CB RC
13 0.675 1.23e- 2 1.07 TRUE GH WL
14 0.731 1.00e-11 2.79 TRUE HN CB
15 0.836 0. 2.81 TRUE HN HT
16 0.781 8.75e- 6 2.11 TRUE HT AW
17 0.757 2.48e- 6 2.09 TRUE HT GH
18 0.891 3.08e- 4 2.53 TRUE HT LB
19 0.852 3.56e- 4 2.94 FALSE HT MI
20 0.619 1.00e+ 0 0.0459 FALSE HT NO
21 0.830 0. 2.74 TRUE HT PM
22 0.694 3.59e- 6 2.23 TRUE HT RT
23 0.642 7.49e- 4 1.55 FALSE HT WL
24 0.878 2.57e-10 3.39 TRUE HT ZB
25 0.724 8.05e- 6 1.94 FALSE LB CB
26 0.710 3.73e- 4 1.50 TRUE LB MI
27 0.745 0. 4.15 TRUE MI AW
28 0.870 3.18e- 4 2.94 FALSE MI NO
29 0.706 1.97e-11 3.38 TRUE MI OK
30 0.793 0. 4.18 TRUE MI RT
31 0.801 8.88e-16 3.49 TRUE MI ZB
32 0.607 5.46e- 2 1.12 TRUE RT WL
33 0.805 4.36e- 4 2.06 TRUE SI AD
34 0.734 5.55e-16 4.01 TRUE SI AW
35 0.741 4.13e- 9 3.18 TRUE SI BT
36 0.736 9.94e-12 3.18 TRUE SI CB
37 0.800 2.11e-15 3.93 TRUE SI GH
38 0.765 7.13e- 1 0.576 TRUE SI HN
39 0.843 6.99e- 4 2.81 TRUE SI HT
40 0.721 6.40e- 3 1.55 TRUE SI LB
41 0.857 4.30e- 3 2.80 TRUE SI NO
42 0.706 1.91e-10 3.17 TRUE SI OK
43 0.843 4.25e-14 3.86 TRUE SI PL
44 0.784 3.82e- 9 2.54 TRUE SI PM
45 0.692 1.30e- 8 2.94 TRUE SI RC
46 0.777 2.22e-16 4.05 TRUE SI RT
47 0.777 1.11e-15 3.51 TRUE SI ZB
48 0.830 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-125.8 -112.7 69.9 -139.8 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.78515 -0.64950 -0.08552 0.71636 2.04577
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001417 0.03764
PORT (Intercept) 0.000000 0.00000
Residual 0.002249 0.04742
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.713165 0.028555 24.975
B_FON_NOECO -0.035042 0.037181 -0.942
PRED_ENV 0.029323 0.009317 3.147
ECO_DIFFTRUE -0.023393 0.020320 -1.151
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.553
PRED_ENV -0.709 0.493
ECO_DIFFTRU -0.389 0.100 -0.227
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.71316479 0.028554834 24.9752733
B_FON_NOECO -0.03504156 0.037180762 -0.9424647
PRED_ENV 0.02932307 0.009317481 3.1471033
ECO_DIFFTRUE -0.02339287 0.020320091 -1.1512186
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -126.93 -115.70 69.464 -138.93
full_model 7 -125.80 -112.71 69.902 -139.80 0.8767 1 0.3491
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "12" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.716 0. 1.06 TRUE AW WL
2 0.625 6.06e- 1 1.56 TRUE BT AW
3 0.705 7.58e- 2 1.50 TRUE BT GH
4 0.800 4.83e- 2 1.52 FALSE BT HT
5 0.777 8.92e- 4 2.14 TRUE BT LB
6 0.750 6.07e- 8 3.16 FALSE BT MI
7 0.795 1.83e- 1 1.56 FALSE BT NO
8 0.735 5.48e- 1 1.57 TRUE BT RT
9 0.733 9.60e- 1 0.921 FALSE BT WL
10 0.776 5.75e- 2 2.25 TRUE BT ZB
11 0.840 2.28e- 2 1.29 FALSE CB PL
12 0.675 2.07e- 4 0.547 FALSE CB RC
13 0.675 1.23e- 2 1.07 TRUE GH WL
14 0.731 1.00e-11 2.79 TRUE HN CB
15 0.836 0. 2.81 TRUE HN HT
16 0.781 8.75e- 6 2.11 TRUE HT AW
17 0.757 2.48e- 6 2.09 TRUE HT GH
18 0.891 3.08e- 4 2.53 TRUE HT LB
19 0.852 3.56e- 4 2.94 FALSE HT MI
20 0.619 1.00e+ 0 0.0459 FALSE HT NO
21 0.830 0. 2.74 TRUE HT PM
22 0.694 3.59e- 6 2.23 TRUE HT RT
23 0.642 7.49e- 4 1.55 FALSE HT WL
24 0.878 2.57e-10 3.39 TRUE HT ZB
25 0.724 8.05e- 6 1.94 FALSE LB CB
26 0.710 3.73e- 4 1.50 TRUE LB MI
27 0.745 0. 4.15 TRUE MI AW
28 0.870 3.18e- 4 2.94 FALSE MI NO
29 0.706 1.97e-11 3.38 TRUE MI OK
30 0.793 0. 4.18 TRUE MI RT
31 0.801 8.88e-16 3.49 TRUE MI ZB
32 0.607 5.46e- 2 1.12 TRUE RT WL
33 0.805 4.36e- 4 2.06 TRUE SI AD
34 0.734 5.55e-16 4.01 TRUE SI AW
35 0.741 4.13e- 9 3.18 TRUE SI BT
36 0.736 9.94e-12 3.18 TRUE SI CB
37 0.800 2.11e-15 3.93 TRUE SI GH
38 0.765 7.13e- 1 0.576 TRUE SI HN
39 0.843 6.99e- 4 2.81 TRUE SI HT
40 0.721 6.40e- 3 1.55 TRUE SI LB
41 0.857 4.30e- 3 2.80 TRUE SI NO
42 0.706 1.91e-10 3.17 TRUE SI OK
43 0.843 4.25e-14 3.86 TRUE SI PL
44 0.784 3.82e- 9 2.54 TRUE SI PM
45 0.692 1.30e- 8 2.94 TRUE SI RC
46 0.777 2.22e-16 4.05 TRUE SI RT
47 0.777 1.11e-15 3.51 TRUE SI ZB
48 0.830 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-125.8 -112.7 69.9 -139.8 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.78515 -0.64950 -0.08552 0.71636 2.04577
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.001417 0.03764
PORT (Intercept) 0.000000 0.00000
Residual 0.002249 0.04742
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.713165 0.028555 24.975
B_FON_NOECO -0.035042 0.037181 -0.942
PRED_ENV 0.029323 0.009317 3.147
ECO_DIFFTRUE -0.023393 0.020320 -1.151
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.553
PRED_ENV -0.709 0.493
ECO_DIFFTRU -0.389 0.100 -0.227
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.71316479 0.028554834 24.9752733
B_FON_NOECO -0.03504156 0.037180762 -0.9424647
PRED_ENV 0.02932307 0.009317481 3.1471033
ECO_DIFFTRUE -0.02339287 0.020320091 -1.1512186
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -126.93 -115.70 69.464 -138.93
full_model 7 -125.80 -112.71 69.902 -139.80 0.8767 1 0.3491
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "13" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.951 0. 1.06 TRUE AW WL
2 0.902 6.06e- 1 1.56 TRUE BT AW
3 0.943 7.58e- 2 1.50 TRUE BT GH
4 0.989 4.83e- 2 1.52 FALSE BT HT
5 0.964 8.92e- 4 2.14 TRUE BT LB
6 0.974 6.07e- 8 3.16 FALSE BT MI
7 0.985 1.83e- 1 1.56 FALSE BT NO
8 0.955 5.48e- 1 1.57 TRUE BT RT
9 0.957 9.60e- 1 0.921 FALSE BT WL
10 0.956 5.75e- 2 2.25 TRUE BT ZB
11 1 2.28e- 2 1.29 FALSE CB PL
12 0.907 2.07e- 4 0.547 FALSE CB RC
13 0.913 1.23e- 2 1.07 TRUE GH WL
14 0.980 1.00e-11 2.79 TRUE HN CB
15 0.993 0. 2.81 TRUE HN HT
16 0.988 8.75e- 6 2.11 TRUE HT AW
17 0.969 2.48e- 6 2.09 TRUE HT GH
18 1.00 3.08e- 4 2.53 TRUE HT LB
19 0.994 3.56e- 4 2.94 FALSE HT MI
20 0.891 1.00e+ 0 0.0459 FALSE HT NO
21 0.997 0. 2.74 TRUE HT PM
22 0.948 3.59e- 6 2.23 TRUE HT RT
23 0.905 7.49e- 4 1.55 FALSE HT WL
24 0.998 2.57e-10 3.39 TRUE HT ZB
25 0.939 8.05e- 6 1.94 FALSE LB CB
26 0.942 3.73e- 4 1.50 TRUE LB MI
27 0.988 0. 4.15 TRUE MI AW
28 0.998 3.18e- 4 2.94 FALSE MI NO
29 0.962 1.97e-11 3.38 TRUE MI OK
30 0.991 0. 4.18 TRUE MI RT
31 0.988 8.88e-16 3.49 TRUE MI ZB
32 0.894 5.46e- 2 1.12 TRUE RT WL
33 0.971 4.36e- 4 2.06 TRUE SI AD
34 0.986 5.55e-16 4.01 TRUE SI AW
35 0.973 4.13e- 9 3.18 TRUE SI BT
36 0.982 9.94e-12 3.18 TRUE SI CB
37 0.996 2.11e-15 3.93 TRUE SI GH
38 0.958 7.13e- 1 0.576 TRUE SI HN
39 0.997 6.99e- 4 2.81 TRUE SI HT
40 0.966 6.40e- 3 1.55 TRUE SI LB
41 0.998 4.30e- 3 2.80 TRUE SI NO
42 0.959 1.91e-10 3.17 TRUE SI OK
43 0.998 4.25e-14 3.86 TRUE SI PL
44 0.996 3.82e- 9 2.54 TRUE SI PM
45 0.967 1.30e- 8 2.94 TRUE SI RC
46 0.993 2.22e-16 4.05 TRUE SI RT
47 0.986 1.11e-15 3.51 TRUE SI ZB
48 0.996 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-215.9 -202.8 115.0 -229.9 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.069 -0.475 -0.122 0.617 1.951
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 8.983e-05 0.009478
PORT (Intercept) 0.000e+00 0.000000
Residual 4.127e-04 0.020315
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.928304 0.011078 83.796
B_FON_NOECO -0.009205 0.015287 -0.602
PRED_ENV 0.019296 0.003783 5.100
ECO_DIFFTRUE -0.005945 0.008185 -0.726
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.570
PRED_ENV -0.732 0.495
ECO_DIFFTRU -0.374 0.080 -0.254
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.928303817 0.011078149 83.7959281
B_FON_NOECO -0.009204673 0.015286957 -0.6021259
PRED_ENV 0.019295847 0.003783291 5.1002812
ECO_DIFFTRUE -0.005944778 0.008185241 -0.7262801
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.57 -206.34 114.79 -229.57
full_model 7 -215.93 -202.84 114.97 -229.93 0.3612 1 0.5478
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "14" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.951 0. 1.06 TRUE AW WL
2 0.902 6.06e- 1 1.56 TRUE BT AW
3 0.943 7.58e- 2 1.50 TRUE BT GH
4 0.989 4.83e- 2 1.52 FALSE BT HT
5 0.964 8.92e- 4 2.14 TRUE BT LB
6 0.974 6.07e- 8 3.16 FALSE BT MI
7 0.985 1.83e- 1 1.56 FALSE BT NO
8 0.955 5.48e- 1 1.57 TRUE BT RT
9 0.957 9.60e- 1 0.921 FALSE BT WL
10 0.956 5.75e- 2 2.25 TRUE BT ZB
11 1 2.28e- 2 1.29 FALSE CB PL
12 0.907 2.07e- 4 0.547 FALSE CB RC
13 0.913 1.23e- 2 1.07 TRUE GH WL
14 0.980 1.00e-11 2.79 TRUE HN CB
15 0.993 0. 2.81 TRUE HN HT
16 0.988 8.75e- 6 2.11 TRUE HT AW
17 0.969 2.48e- 6 2.09 TRUE HT GH
18 1.00 3.08e- 4 2.53 TRUE HT LB
19 0.994 3.56e- 4 2.94 FALSE HT MI
20 0.891 1.00e+ 0 0.0459 FALSE HT NO
21 0.997 0. 2.74 TRUE HT PM
22 0.948 3.59e- 6 2.23 TRUE HT RT
23 0.905 7.49e- 4 1.55 FALSE HT WL
24 0.998 2.57e-10 3.39 TRUE HT ZB
25 0.939 8.05e- 6 1.94 FALSE LB CB
26 0.942 3.73e- 4 1.50 TRUE LB MI
27 0.988 0. 4.15 TRUE MI AW
28 0.998 3.18e- 4 2.94 FALSE MI NO
29 0.962 1.97e-11 3.38 TRUE MI OK
30 0.991 0. 4.18 TRUE MI RT
31 0.988 8.88e-16 3.49 TRUE MI ZB
32 0.894 5.46e- 2 1.12 TRUE RT WL
33 0.971 4.36e- 4 2.06 TRUE SI AD
34 0.986 5.55e-16 4.01 TRUE SI AW
35 0.973 4.13e- 9 3.18 TRUE SI BT
36 0.982 9.94e-12 3.18 TRUE SI CB
37 0.996 2.11e-15 3.93 TRUE SI GH
38 0.958 7.13e- 1 0.576 TRUE SI HN
39 0.997 6.99e- 4 2.81 TRUE SI HT
40 0.966 6.40e- 3 1.55 TRUE SI LB
41 0.998 4.30e- 3 2.80 TRUE SI NO
42 0.959 1.91e-10 3.17 TRUE SI OK
43 0.998 4.25e-14 3.86 TRUE SI PL
44 0.996 3.82e- 9 2.54 TRUE SI PM
45 0.967 1.30e- 8 2.94 TRUE SI RC
46 0.993 2.22e-16 4.05 TRUE SI RT
47 0.986 1.11e-15 3.51 TRUE SI ZB
48 0.996 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-215.9 -202.8 115.0 -229.9 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.069 -0.475 -0.122 0.617 1.951
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 8.983e-05 0.009478
PORT (Intercept) 0.000e+00 0.000000
Residual 4.127e-04 0.020315
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.928304 0.011078 83.796
B_FON_NOECO -0.009205 0.015287 -0.602
PRED_ENV 0.019296 0.003783 5.100
ECO_DIFFTRUE -0.005945 0.008185 -0.726
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.570
PRED_ENV -0.732 0.495
ECO_DIFFTRU -0.374 0.080 -0.254
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.928303817 0.011078149 83.7959281
B_FON_NOECO -0.009204673 0.015286957 -0.6021259
PRED_ENV 0.019295847 0.003783291 5.1002812
ECO_DIFFTRUE -0.005944778 0.008185241 -0.7262801
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.57 -206.34 114.79 -229.57
full_model 7 -215.93 -202.84 114.97 -229.93 0.3612 1 0.5478
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "15" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 0. 1.06 TRUE AW WL
2 0.847 6.06e- 1 1.56 TRUE BT AW
3 0.906 7.58e- 2 1.50 TRUE BT GH
4 0.980 4.83e- 2 1.52 FALSE BT HT
5 0.936 8.92e- 4 2.14 TRUE BT LB
6 0.933 6.07e- 8 3.16 FALSE BT MI
7 0.971 1.83e- 1 1.56 FALSE BT NO
8 0.927 5.48e- 1 1.57 TRUE BT RT
9 0.934 9.60e- 1 0.921 FALSE BT WL
10 0.931 5.75e- 2 2.25 TRUE BT ZB
11 0.998 2.28e- 2 1.29 FALSE CB PL
12 0.863 2.07e- 4 0.547 FALSE CB RC
13 0.879 1.23e- 2 1.07 TRUE GH WL
14 0.947 1.00e-11 2.79 TRUE HN CB
15 0.988 0. 2.81 TRUE HN HT
16 0.981 8.75e- 6 2.11 TRUE HT AW
17 0.951 2.48e- 6 2.09 TRUE HT GH
18 0.998 3.08e- 4 2.53 TRUE HT LB
19 0.987 3.56e- 4 2.94 FALSE HT MI
20 0.848 1.00e+ 0 0.0459 FALSE HT NO
21 0.995 0. 2.74 TRUE HT PM
22 0.920 3.59e- 6 2.23 TRUE HT RT
23 0.865 7.49e- 4 1.55 FALSE HT WL
24 0.997 2.57e-10 3.39 TRUE HT ZB
25 0.901 8.05e- 6 1.94 FALSE LB CB
26 0.907 3.73e- 4 1.50 TRUE LB MI
27 0.966 0. 4.15 TRUE MI AW
28 0.990 3.18e- 4 2.94 FALSE MI NO
29 0.927 1.97e-11 3.38 TRUE MI OK
30 0.976 0. 4.18 TRUE MI RT
31 0.965 8.88e-16 3.49 TRUE MI ZB
32 0.839 5.46e- 2 1.12 TRUE RT WL
33 0.945 4.36e- 4 2.06 TRUE SI AD
34 0.960 5.55e-16 4.01 TRUE SI AW
35 0.947 4.13e- 9 3.18 TRUE SI BT
36 0.950 9.94e-12 3.18 TRUE SI CB
37 0.986 2.11e-15 3.93 TRUE SI GH
38 0.935 7.13e- 1 0.576 TRUE SI HN
39 0.994 6.99e- 4 2.81 TRUE SI HT
40 0.931 6.40e- 3 1.55 TRUE SI LB
41 0.995 4.30e- 3 2.80 TRUE SI NO
42 0.919 1.91e-10 3.17 TRUE SI OK
43 0.997 4.25e-14 3.86 TRUE SI PL
44 0.983 3.82e- 9 2.54 TRUE SI PM
45 0.928 1.30e- 8 2.94 TRUE SI RC
46 0.976 2.22e-16 4.05 TRUE SI RT
47 0.952 1.11e-15 3.51 TRUE SI ZB
48 0.987 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-174.8 -161.7 94.4 -188.8 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.93141 -0.51558 -0.09184 0.81272 1.77162
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0003072 0.01753
PORT (Intercept) 0.0000000 0.00000
Residual 0.0009117 0.03019
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.896467 0.017088 52.463
B_FON_NOECO -0.014710 0.023088 -0.637
PRED_ENV 0.024481 0.005748 4.259
ECO_DIFFTRUE -0.010036 0.012466 -0.805
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.565
PRED_ENV -0.725 0.495
ECO_DIFFTRU -0.381 0.087 -0.243
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.89646675 0.017087632 52.4628999
B_FON_NOECO -0.01471004 0.023088437 -0.6371172
PRED_ENV 0.02448060 0.005747735 4.2591730
ECO_DIFFTRUE -0.01003636 0.012465984 -0.8051000
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -176.43 -165.20 94.215 -188.43
full_model 7 -174.83 -161.74 94.417 -188.83 0.4026 1 0.5258
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "16" and formula index FIDX "3" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 0. 1.06 TRUE AW WL
2 0.847 6.06e- 1 1.56 TRUE BT AW
3 0.906 7.58e- 2 1.50 TRUE BT GH
4 0.980 4.83e- 2 1.52 FALSE BT HT
5 0.936 8.92e- 4 2.14 TRUE BT LB
6 0.933 6.07e- 8 3.16 FALSE BT MI
7 0.971 1.83e- 1 1.56 FALSE BT NO
8 0.927 5.48e- 1 1.57 TRUE BT RT
9 0.934 9.60e- 1 0.921 FALSE BT WL
10 0.931 5.75e- 2 2.25 TRUE BT ZB
11 0.998 2.28e- 2 1.29 FALSE CB PL
12 0.863 2.07e- 4 0.547 FALSE CB RC
13 0.879 1.23e- 2 1.07 TRUE GH WL
14 0.947 1.00e-11 2.79 TRUE HN CB
15 0.988 0. 2.81 TRUE HN HT
16 0.981 8.75e- 6 2.11 TRUE HT AW
17 0.951 2.48e- 6 2.09 TRUE HT GH
18 0.998 3.08e- 4 2.53 TRUE HT LB
19 0.987 3.56e- 4 2.94 FALSE HT MI
20 0.848 1.00e+ 0 0.0459 FALSE HT NO
21 0.995 0. 2.74 TRUE HT PM
22 0.920 3.59e- 6 2.23 TRUE HT RT
23 0.865 7.49e- 4 1.55 FALSE HT WL
24 0.997 2.57e-10 3.39 TRUE HT ZB
25 0.901 8.05e- 6 1.94 FALSE LB CB
26 0.907 3.73e- 4 1.50 TRUE LB MI
27 0.966 0. 4.15 TRUE MI AW
28 0.990 3.18e- 4 2.94 FALSE MI NO
29 0.927 1.97e-11 3.38 TRUE MI OK
30 0.976 0. 4.18 TRUE MI RT
31 0.965 8.88e-16 3.49 TRUE MI ZB
32 0.839 5.46e- 2 1.12 TRUE RT WL
33 0.945 4.36e- 4 2.06 TRUE SI AD
34 0.960 5.55e-16 4.01 TRUE SI AW
35 0.947 4.13e- 9 3.18 TRUE SI BT
36 0.950 9.94e-12 3.18 TRUE SI CB
37 0.986 2.11e-15 3.93 TRUE SI GH
38 0.935 7.13e- 1 0.576 TRUE SI HN
39 0.994 6.99e- 4 2.81 TRUE SI HT
40 0.931 6.40e- 3 1.55 TRUE SI LB
41 0.995 4.30e- 3 2.80 TRUE SI NO
42 0.919 1.91e-10 3.17 TRUE SI OK
43 0.997 4.25e-14 3.86 TRUE SI PL
44 0.983 3.82e- 9 2.54 TRUE SI PM
45 0.928 1.30e- 8 2.94 TRUE SI RC
46 0.976 2.22e-16 4.05 TRUE SI RT
47 0.952 1.11e-15 3.51 TRUE SI ZB
48 0.987 8.20e- 6 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-174.8 -161.7 94.4 -188.8 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.93141 -0.51558 -0.09184 0.81272 1.77162
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0003072 0.01753
PORT (Intercept) 0.0000000 0.00000
Residual 0.0009117 0.03019
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.896467 0.017088 52.463
B_FON_NOECO -0.014710 0.023088 -0.637
PRED_ENV 0.024481 0.005748 4.259
ECO_DIFFTRUE -0.010036 0.012466 -0.805
Correlation of Fixed Effects:
(Intr) B_FON_ PRED_E
B_FON_NOECO -0.565
PRED_ENV -0.725 0.495
ECO_DIFFTRU -0.381 0.087 -0.243
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.89646675 0.017087632 52.4628999
B_FON_NOECO -0.01471004 0.023088437 -0.6371172
PRED_ENV 0.02448060 0.005747735 4.2591730
ECO_DIFFTRUE -0.01003636 0.012465984 -0.8051000
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -176.43 -165.20 94.215 -188.43
full_model 7 -174.83 -161.74 94.417 -188.83 0.4026 1 0.5258
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "1" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.700 1.14e- 4 1.06 TRUE AW WL
2 0.636 5.55e- 3 1.56 TRUE BT AW
3 0.695 6.70e- 4 1.50 TRUE BT GH
4 0.794 1.01e- 4 1.52 FALSE BT HT
5 0.785 1.02e- 5 2.14 TRUE BT LB
6 0.756 9.45e-11 3.16 FALSE BT MI
7 0.794 1.77e- 4 1.56 FALSE BT NO
8 0.732 2.80e- 3 1.57 TRUE BT RT
9 0.723 8.76e- 3 0.921 FALSE BT WL
10 0.776 1.49e- 4 2.25 TRUE BT ZB
11 0.832 1.48e- 5 1.29 FALSE CB PL
12 0.683 4.06e- 5 0.547 FALSE CB RC
13 0.654 2.96e- 4 1.07 TRUE GH WL
14 0.739 3.92e-12 2.79 TRUE HN CB
15 0.829 0. 2.81 TRUE HN HT
16 0.774 2.51e- 8 2.11 TRUE HT AW
17 0.747 8.88e- 8 2.09 TRUE HT GH
18 0.885 1.47e- 6 2.53 TRUE HT LB
19 0.845 8.29e- 7 2.94 FALSE HT MI
20 0.628 1.00e+ 0 0.0459 FALSE HT NO
21 0.828 0. 2.74 TRUE HT PM
22 0.695 1.60e- 8 2.23 TRUE HT RT
23 0.639 3.73e- 6 1.55 FALSE HT WL
24 0.869 0. 3.39 TRUE HT ZB
25 0.738 1.26e- 7 1.94 FALSE LB CB
26 0.726 8.69e- 6 1.50 TRUE LB MI
27 0.748 0. 4.15 TRUE MI AW
28 0.864 1.06e- 6 2.94 FALSE MI NO
29 0.712 0. 3.38 TRUE MI OK
30 0.786 0. 4.18 TRUE MI RT
31 0.799 0. 3.49 TRUE MI ZB
32 0.603 1.44e- 3 1.12 TRUE RT WL
33 0.815 4.13e- 6 2.06 TRUE SI AD
34 0.740 0. 4.01 TRUE SI AW
35 0.748 6.55e-11 3.18 TRUE SI BT
36 0.724 0. 3.18 TRUE SI CB
37 0.789 0. 3.93 TRUE SI GH
38 0.762 8.94e- 3 0.576 TRUE SI HN
39 0.836 1.00e- 5 2.81 TRUE SI HT
40 0.730 9.72e- 5 1.55 TRUE SI LB
41 0.853 3.21e- 5 2.80 TRUE SI NO
42 0.692 1.75e-12 3.17 TRUE SI OK
43 0.835 0. 3.86 TRUE SI PL
44 0.780 1.05e-10 2.54 TRUE SI PM
45 0.691 1.44e-10 2.94 TRUE SI RC
46 0.774 0. 4.05 TRUE SI RT
47 0.789 0. 3.51 TRUE SI ZB
48 0.817 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-140.1 -127.0 77.0 -154.1 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.90179 -0.46056 -0.08439 0.59392 2.02672
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 2.077e-03 0.045572
PORT (Intercept) 9.894e-05 0.009947
Residual 1.252e-03 0.035384
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.722667 0.023240 31.096
B_HON_NOECO -0.158283 0.044268 -3.576
PRED_ENV 0.025646 0.007243 3.541
ECO_DIFFTRUE -0.023867 0.016533 -1.444
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.321
PRED_ENV -0.621 0.352
ECO_DIFFTRU -0.415 0.066 -0.189
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.72266677 0.023239743 31.096160
B_HON_NOECO -0.15828288 0.044268219 -3.575542
PRED_ENV 0.02564636 0.007242979 3.540858
ECO_DIFFTRUE -0.02386716 0.016533016 -1.443606
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.05 -121.82 72.525 -145.05
full_model 7 -140.07 -126.98 77.037 -154.07 9.0252 1 0.002663 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients:
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "2" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.700 1.14e- 4 1.06 TRUE AW WL
2 0.636 5.55e- 3 1.56 TRUE BT AW
3 0.695 6.70e- 4 1.50 TRUE BT GH
4 0.794 1.01e- 4 1.52 FALSE BT HT
5 0.785 1.02e- 5 2.14 TRUE BT LB
6 0.756 9.45e-11 3.16 FALSE BT MI
7 0.794 1.77e- 4 1.56 FALSE BT NO
8 0.732 2.80e- 3 1.57 TRUE BT RT
9 0.723 8.76e- 3 0.921 FALSE BT WL
10 0.776 1.49e- 4 2.25 TRUE BT ZB
11 0.832 1.48e- 5 1.29 FALSE CB PL
12 0.683 4.06e- 5 0.547 FALSE CB RC
13 0.654 2.96e- 4 1.07 TRUE GH WL
14 0.739 3.92e-12 2.79 TRUE HN CB
15 0.829 0. 2.81 TRUE HN HT
16 0.774 2.51e- 8 2.11 TRUE HT AW
17 0.747 8.88e- 8 2.09 TRUE HT GH
18 0.885 1.47e- 6 2.53 TRUE HT LB
19 0.845 8.29e- 7 2.94 FALSE HT MI
20 0.628 1.00e+ 0 0.0459 FALSE HT NO
21 0.828 0. 2.74 TRUE HT PM
22 0.695 1.60e- 8 2.23 TRUE HT RT
23 0.639 3.73e- 6 1.55 FALSE HT WL
24 0.869 0. 3.39 TRUE HT ZB
25 0.738 1.26e- 7 1.94 FALSE LB CB
26 0.726 8.69e- 6 1.50 TRUE LB MI
27 0.748 0. 4.15 TRUE MI AW
28 0.864 1.06e- 6 2.94 FALSE MI NO
29 0.712 0. 3.38 TRUE MI OK
30 0.786 0. 4.18 TRUE MI RT
31 0.799 0. 3.49 TRUE MI ZB
32 0.603 1.44e- 3 1.12 TRUE RT WL
33 0.815 4.13e- 6 2.06 TRUE SI AD
34 0.740 0. 4.01 TRUE SI AW
35 0.748 6.55e-11 3.18 TRUE SI BT
36 0.724 0. 3.18 TRUE SI CB
37 0.789 0. 3.93 TRUE SI GH
38 0.762 8.94e- 3 0.576 TRUE SI HN
39 0.836 1.00e- 5 2.81 TRUE SI HT
40 0.730 9.72e- 5 1.55 TRUE SI LB
41 0.853 3.21e- 5 2.80 TRUE SI NO
42 0.692 1.75e-12 3.17 TRUE SI OK
43 0.835 0. 3.86 TRUE SI PL
44 0.780 1.05e-10 2.54 TRUE SI PM
45 0.691 1.44e-10 2.94 TRUE SI RC
46 0.774 0. 4.05 TRUE SI RT
47 0.789 0. 3.51 TRUE SI ZB
48 0.817 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-140.1 -127.0 77.0 -154.1 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.90179 -0.46056 -0.08439 0.59392 2.02672
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 2.077e-03 0.045572
PORT (Intercept) 9.894e-05 0.009947
Residual 1.252e-03 0.035384
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.722667 0.023240 31.096
B_HON_NOECO -0.158283 0.044268 -3.576
PRED_ENV 0.025646 0.007243 3.541
ECO_DIFFTRUE -0.023867 0.016533 -1.444
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.321
PRED_ENV -0.621 0.352
ECO_DIFFTRU -0.415 0.066 -0.189
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.72266675 0.023239742 31.096161
B_HON_NOECO -0.15828284 0.044268215 -3.575541
PRED_ENV 0.02564636 0.007242979 3.540858
ECO_DIFFTRUE -0.02386715 0.016533016 -1.443606
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.05 -121.82 72.525 -145.05
full_model 7 -140.07 -126.98 77.037 -154.07 9.0252 1 0.002663 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "3" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.708 1.14e- 4 1.06 TRUE AW WL
2 0.634 5.55e- 3 1.56 TRUE BT AW
3 0.708 6.70e- 4 1.50 TRUE BT GH
4 0.800 1.01e- 4 1.52 FALSE BT HT
5 0.791 1.02e- 5 2.14 TRUE BT LB
6 0.751 9.45e-11 3.16 FALSE BT MI
7 0.802 1.77e- 4 1.56 FALSE BT NO
8 0.732 2.80e- 3 1.57 TRUE BT RT
9 0.740 8.76e- 3 0.921 FALSE BT WL
10 0.786 1.49e- 4 2.25 TRUE BT ZB
11 0.831 1.48e- 5 1.29 FALSE CB PL
12 0.688 4.06e- 5 0.547 FALSE CB RC
13 0.671 2.96e- 4 1.07 TRUE GH WL
14 0.736 3.92e-12 2.79 TRUE HN CB
15 0.830 0. 2.81 TRUE HN HT
16 0.781 2.51e- 8 2.11 TRUE HT AW
17 0.753 8.88e- 8 2.09 TRUE HT GH
18 0.892 1.47e- 6 2.53 TRUE HT LB
19 0.851 8.29e- 7 2.94 FALSE HT MI
20 0.635 1.00e+ 0 0.0459 FALSE HT NO
21 0.824 0. 2.74 TRUE HT PM
22 0.700 1.60e- 8 2.23 TRUE HT RT
23 0.644 3.73e- 6 1.55 FALSE HT WL
24 0.879 0. 3.39 TRUE HT ZB
25 0.730 1.26e- 7 1.94 FALSE LB CB
26 0.726 8.69e- 6 1.50 TRUE LB MI
27 0.747 0. 4.15 TRUE MI AW
28 0.870 1.06e- 6 2.94 FALSE MI NO
29 0.715 0. 3.38 TRUE MI OK
30 0.789 0. 4.18 TRUE MI RT
31 0.802 0. 3.49 TRUE MI ZB
32 0.607 1.44e- 3 1.12 TRUE RT WL
33 0.815 4.13e- 6 2.06 TRUE SI AD
34 0.737 0. 4.01 TRUE SI AW
35 0.747 6.55e-11 3.18 TRUE SI BT
36 0.724 0. 3.18 TRUE SI CB
37 0.790 0. 3.93 TRUE SI GH
38 0.768 8.94e- 3 0.576 TRUE SI HN
39 0.837 1.00e- 5 2.81 TRUE SI HT
40 0.732 9.72e- 5 1.55 TRUE SI LB
41 0.851 3.21e- 5 2.80 TRUE SI NO
42 0.698 1.75e-12 3.17 TRUE SI OK
43 0.836 0. 3.86 TRUE SI PL
44 0.780 1.05e-10 2.54 TRUE SI PM
45 0.702 1.44e-10 2.94 TRUE SI RC
46 0.774 0. 4.05 TRUE SI RT
47 0.786 0. 3.51 TRUE SI ZB
48 0.821 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-137.7 -124.6 75.9 -151.7 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.93942 -0.43001 -0.01406 0.52378 2.04698
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 1.938e-03 0.044026
PORT (Intercept) 9.361e-05 0.009675
Residual 1.385e-03 0.037217
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.728026 0.023645 30.790
B_HON_NOECO -0.155901 0.046259 -3.370
PRED_ENV 0.024451 0.007484 3.267
ECO_DIFFTRUE -0.023820 0.017184 -1.386
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.326
PRED_ENV -0.629 0.347
ECO_DIFFTRU -0.417 0.068 -0.199
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.72802610 0.023645089 30.789739
B_HON_NOECO -0.15590069 0.046259288 -3.370149
PRED_ENV 0.02445149 0.007484109 3.267120
ECO_DIFFTRUE -0.02381975 0.017184426 -1.386124
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -131.56 -120.33 71.780 -143.56
full_model 7 -137.73 -124.63 75.865 -151.73 8.1705 1 0.004258 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "4" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.708 1.14e- 4 1.06 TRUE AW WL
2 0.634 5.55e- 3 1.56 TRUE BT AW
3 0.708 6.70e- 4 1.50 TRUE BT GH
4 0.800 1.01e- 4 1.52 FALSE BT HT
5 0.791 1.02e- 5 2.14 TRUE BT LB
6 0.751 9.45e-11 3.16 FALSE BT MI
7 0.802 1.77e- 4 1.56 FALSE BT NO
8 0.732 2.80e- 3 1.57 TRUE BT RT
9 0.740 8.76e- 3 0.921 FALSE BT WL
10 0.786 1.49e- 4 2.25 TRUE BT ZB
11 0.831 1.48e- 5 1.29 FALSE CB PL
12 0.688 4.06e- 5 0.547 FALSE CB RC
13 0.671 2.96e- 4 1.07 TRUE GH WL
14 0.736 3.92e-12 2.79 TRUE HN CB
15 0.830 0. 2.81 TRUE HN HT
16 0.781 2.51e- 8 2.11 TRUE HT AW
17 0.753 8.88e- 8 2.09 TRUE HT GH
18 0.892 1.47e- 6 2.53 TRUE HT LB
19 0.851 8.29e- 7 2.94 FALSE HT MI
20 0.635 1.00e+ 0 0.0459 FALSE HT NO
21 0.824 0. 2.74 TRUE HT PM
22 0.700 1.60e- 8 2.23 TRUE HT RT
23 0.644 3.73e- 6 1.55 FALSE HT WL
24 0.879 0. 3.39 TRUE HT ZB
25 0.730 1.26e- 7 1.94 FALSE LB CB
26 0.726 8.69e- 6 1.50 TRUE LB MI
27 0.747 0. 4.15 TRUE MI AW
28 0.870 1.06e- 6 2.94 FALSE MI NO
29 0.715 0. 3.38 TRUE MI OK
30 0.789 0. 4.18 TRUE MI RT
31 0.802 0. 3.49 TRUE MI ZB
32 0.607 1.44e- 3 1.12 TRUE RT WL
33 0.815 4.13e- 6 2.06 TRUE SI AD
34 0.737 0. 4.01 TRUE SI AW
35 0.747 6.55e-11 3.18 TRUE SI BT
36 0.724 0. 3.18 TRUE SI CB
37 0.790 0. 3.93 TRUE SI GH
38 0.768 8.94e- 3 0.576 TRUE SI HN
39 0.837 1.00e- 5 2.81 TRUE SI HT
40 0.732 9.72e- 5 1.55 TRUE SI LB
41 0.851 3.21e- 5 2.80 TRUE SI NO
42 0.698 1.75e-12 3.17 TRUE SI OK
43 0.836 0. 3.86 TRUE SI PL
44 0.780 1.05e-10 2.54 TRUE SI PM
45 0.702 1.44e-10 2.94 TRUE SI RC
46 0.774 0. 4.05 TRUE SI RT
47 0.786 0. 3.51 TRUE SI ZB
48 0.821 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-137.7 -124.6 75.9 -151.7 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.93942 -0.43001 -0.01406 0.52378 2.04698
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 1.938e-03 0.044026
PORT (Intercept) 9.361e-05 0.009675
Residual 1.385e-03 0.037217
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.728026 0.023645 30.790
B_HON_NOECO -0.155901 0.046259 -3.370
PRED_ENV 0.024451 0.007484 3.267
ECO_DIFFTRUE -0.023820 0.017184 -1.386
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.326
PRED_ENV -0.629 0.347
ECO_DIFFTRU -0.417 0.068 -0.199
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.72802610 0.023645089 30.789739
B_HON_NOECO -0.15590069 0.046259288 -3.370149
PRED_ENV 0.02445149 0.007484109 3.267120
ECO_DIFFTRUE -0.02381975 0.017184426 -1.386124
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -131.56 -120.33 71.780 -143.56
full_model 7 -137.73 -124.63 75.865 -151.73 8.1705 1 0.004258 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "5" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.950 1.14e- 4 1.06 TRUE AW WL
2 0.902 5.55e- 3 1.56 TRUE BT AW
3 0.942 6.70e- 4 1.50 TRUE BT GH
4 0.989 1.01e- 4 1.52 FALSE BT HT
5 0.964 1.02e- 5 2.14 TRUE BT LB
6 0.974 9.45e-11 3.16 FALSE BT MI
7 0.985 1.77e- 4 1.56 FALSE BT NO
8 0.955 2.80e- 3 1.57 TRUE BT RT
9 0.958 8.76e- 3 0.921 FALSE BT WL
10 0.955 1.49e- 4 2.25 TRUE BT ZB
11 1 1.48e- 5 1.29 FALSE CB PL
12 0.908 4.06e- 5 0.547 FALSE CB RC
13 0.913 2.96e- 4 1.07 TRUE GH WL
14 0.980 3.92e-12 2.79 TRUE HN CB
15 0.993 0. 2.81 TRUE HN HT
16 0.988 2.51e- 8 2.11 TRUE HT AW
17 0.969 8.88e- 8 2.09 TRUE HT GH
18 1.00 1.47e- 6 2.53 TRUE HT LB
19 0.994 8.29e- 7 2.94 FALSE HT MI
20 0.892 1.00e+ 0 0.0459 FALSE HT NO
21 0.997 0. 2.74 TRUE HT PM
22 0.948 1.60e- 8 2.23 TRUE HT RT
23 0.906 3.73e- 6 1.55 FALSE HT WL
24 0.999 0. 3.39 TRUE HT ZB
25 0.940 1.26e- 7 1.94 FALSE LB CB
26 0.943 8.69e- 6 1.50 TRUE LB MI
27 0.988 0. 4.15 TRUE MI AW
28 0.998 1.06e- 6 2.94 FALSE MI NO
29 0.963 0. 3.38 TRUE MI OK
30 0.991 0. 4.18 TRUE MI RT
31 0.988 0. 3.49 TRUE MI ZB
32 0.894 1.44e- 3 1.12 TRUE RT WL
33 0.971 4.13e- 6 2.06 TRUE SI AD
34 0.985 0. 4.01 TRUE SI AW
35 0.973 6.55e-11 3.18 TRUE SI BT
36 0.981 0. 3.18 TRUE SI CB
37 0.995 0. 3.93 TRUE SI GH
38 0.959 8.94e- 3 0.576 TRUE SI HN
39 0.997 1.00e- 5 2.81 TRUE SI HT
40 0.967 9.72e- 5 1.55 TRUE SI LB
41 0.997 3.21e- 5 2.80 TRUE SI NO
42 0.958 1.75e-12 3.17 TRUE SI OK
43 0.998 0. 3.86 TRUE SI PL
44 0.996 1.05e-10 2.54 TRUE SI PM
45 0.965 1.44e-10 2.94 TRUE SI RC
46 0.992 0. 4.05 TRUE SI RT
47 0.984 0. 3.51 TRUE SI ZB
48 0.996 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-221.3 -208.2 117.7 -235.3 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.38107 -0.46947 0.01774 0.58778 1.81593
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001561 0.01249
PORT (Intercept) 0.0000000 0.00000
Residual 0.0003247 0.01802
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.934088 0.009349 99.913
B_HON_NOECO -0.054300 0.021217 -2.559
PRED_ENV 0.017608 0.003198 5.507
ECO_DIFFTRUE -0.007589 0.007592 -1.000
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.351
PRED_ENV -0.654 0.313
ECO_DIFFTRU -0.407 0.085 -0.280
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.934087851 0.009349048 99.9126184
B_HON_NOECO -0.054300291 0.021216761 -2.5593110
PRED_ENV 0.017608080 0.003197524 5.5067863
ECO_DIFFTRUE -0.007588602 0.007592272 -0.9995166
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.82 -206.59 114.91 -229.82
full_model 7 -221.34 -208.25 117.67 -235.34 5.5251 1 0.01875 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "6" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.950 1.14e- 4 1.06 TRUE AW WL
2 0.902 5.55e- 3 1.56 TRUE BT AW
3 0.942 6.70e- 4 1.50 TRUE BT GH
4 0.989 1.01e- 4 1.52 FALSE BT HT
5 0.964 1.02e- 5 2.14 TRUE BT LB
6 0.974 9.45e-11 3.16 FALSE BT MI
7 0.985 1.77e- 4 1.56 FALSE BT NO
8 0.955 2.80e- 3 1.57 TRUE BT RT
9 0.958 8.76e- 3 0.921 FALSE BT WL
10 0.955 1.49e- 4 2.25 TRUE BT ZB
11 1 1.48e- 5 1.29 FALSE CB PL
12 0.908 4.06e- 5 0.547 FALSE CB RC
13 0.913 2.96e- 4 1.07 TRUE GH WL
14 0.980 3.92e-12 2.79 TRUE HN CB
15 0.993 0. 2.81 TRUE HN HT
16 0.988 2.51e- 8 2.11 TRUE HT AW
17 0.969 8.88e- 8 2.09 TRUE HT GH
18 1.00 1.47e- 6 2.53 TRUE HT LB
19 0.994 8.29e- 7 2.94 FALSE HT MI
20 0.892 1.00e+ 0 0.0459 FALSE HT NO
21 0.997 0. 2.74 TRUE HT PM
22 0.948 1.60e- 8 2.23 TRUE HT RT
23 0.906 3.73e- 6 1.55 FALSE HT WL
24 0.999 0. 3.39 TRUE HT ZB
25 0.940 1.26e- 7 1.94 FALSE LB CB
26 0.943 8.69e- 6 1.50 TRUE LB MI
27 0.988 0. 4.15 TRUE MI AW
28 0.998 1.06e- 6 2.94 FALSE MI NO
29 0.963 0. 3.38 TRUE MI OK
30 0.991 0. 4.18 TRUE MI RT
31 0.988 0. 3.49 TRUE MI ZB
32 0.894 1.44e- 3 1.12 TRUE RT WL
33 0.971 4.13e- 6 2.06 TRUE SI AD
34 0.985 0. 4.01 TRUE SI AW
35 0.973 6.55e-11 3.18 TRUE SI BT
36 0.981 0. 3.18 TRUE SI CB
37 0.995 0. 3.93 TRUE SI GH
38 0.959 8.94e- 3 0.576 TRUE SI HN
39 0.997 1.00e- 5 2.81 TRUE SI HT
40 0.967 9.72e- 5 1.55 TRUE SI LB
41 0.997 3.21e- 5 2.80 TRUE SI NO
42 0.958 1.75e-12 3.17 TRUE SI OK
43 0.998 0. 3.86 TRUE SI PL
44 0.996 1.05e-10 2.54 TRUE SI PM
45 0.965 1.44e-10 2.94 TRUE SI RC
46 0.992 0. 4.05 TRUE SI RT
47 0.984 0. 3.51 TRUE SI ZB
48 0.996 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-221.3 -208.2 117.7 -235.3 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.38107 -0.46947 0.01774 0.58778 1.81593
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001561 0.01249
PORT (Intercept) 0.0000000 0.00000
Residual 0.0003247 0.01802
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.934088 0.009349 99.913
B_HON_NOECO -0.054300 0.021217 -2.559
PRED_ENV 0.017608 0.003198 5.507
ECO_DIFFTRUE -0.007589 0.007592 -1.000
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.351
PRED_ENV -0.654 0.313
ECO_DIFFTRU -0.407 0.085 -0.280
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.934087851 0.009349048 99.9126184
B_HON_NOECO -0.054300291 0.021216761 -2.5593110
PRED_ENV 0.017608080 0.003197524 5.5067863
ECO_DIFFTRUE -0.007588602 0.007592272 -0.9995166
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.82 -206.59 114.91 -229.82
full_model 7 -221.34 -208.25 117.67 -235.34 5.5251 1 0.01875 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "7" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 1.14e- 4 1.06 TRUE AW WL
2 0.848 5.55e- 3 1.56 TRUE BT AW
3 0.907 6.70e- 4 1.50 TRUE BT GH
4 0.980 1.01e- 4 1.52 FALSE BT HT
5 0.938 1.02e- 5 2.14 TRUE BT LB
6 0.934 9.45e-11 3.16 FALSE BT MI
7 0.971 1.77e- 4 1.56 FALSE BT NO
8 0.928 2.80e- 3 1.57 TRUE BT RT
9 0.935 8.76e- 3 0.921 FALSE BT WL
10 0.932 1.49e- 4 2.25 TRUE BT ZB
11 0.998 1.48e- 5 1.29 FALSE CB PL
12 0.863 4.06e- 5 0.547 FALSE CB RC
13 0.879 2.96e- 4 1.07 TRUE GH WL
14 0.948 3.92e-12 2.79 TRUE HN CB
15 0.988 0. 2.81 TRUE HN HT
16 0.981 2.51e- 8 2.11 TRUE HT AW
17 0.951 8.88e- 8 2.09 TRUE HT GH
18 0.998 1.47e- 6 2.53 TRUE HT LB
19 0.987 8.29e- 7 2.94 FALSE HT MI
20 0.848 1.00e+ 0 0.0459 FALSE HT NO
21 0.995 0. 2.74 TRUE HT PM
22 0.921 1.60e- 8 2.23 TRUE HT RT
23 0.866 3.73e- 6 1.55 FALSE HT WL
24 0.997 0. 3.39 TRUE HT ZB
25 0.902 1.26e- 7 1.94 FALSE LB CB
26 0.908 8.69e- 6 1.50 TRUE LB MI
27 0.967 0. 4.15 TRUE MI AW
28 0.990 1.06e- 6 2.94 FALSE MI NO
29 0.928 0. 3.38 TRUE MI OK
30 0.977 0. 4.18 TRUE MI RT
31 0.966 0. 3.49 TRUE MI ZB
32 0.840 1.44e- 3 1.12 TRUE RT WL
33 0.946 4.13e- 6 2.06 TRUE SI AD
34 0.960 0. 4.01 TRUE SI AW
35 0.947 6.55e-11 3.18 TRUE SI BT
36 0.950 0. 3.18 TRUE SI CB
37 0.986 0. 3.93 TRUE SI GH
38 0.935 8.94e- 3 0.576 TRUE SI HN
39 0.993 1.00e- 5 2.81 TRUE SI HT
40 0.932 9.72e- 5 1.55 TRUE SI LB
41 0.994 3.21e- 5 2.80 TRUE SI NO
42 0.922 1.75e-12 3.17 TRUE SI OK
43 0.996 0. 3.86 TRUE SI PL
44 0.983 1.05e-10 2.54 TRUE SI PM
45 0.930 1.44e-10 2.94 TRUE SI RC
46 0.976 0. 4.05 TRUE SI RT
47 0.952 0. 3.51 TRUE SI ZB
48 0.987 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-180.2 -167.1 97.1 -194.2 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.3856 -0.4993 -0.0156 0.5616 2.0317
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 4.228e-04 2.056e-02
PORT (Intercept) 7.410e-14 2.722e-07
Residual 7.388e-04 2.718e-02
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.902684 0.014372 62.808
B_HON_NOECO -0.075333 0.032178 -2.341
PRED_ENV 0.022661 0.004874 4.649
ECO_DIFFTRUE -0.011357 0.011562 -0.982
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.347
PRED_ENV -0.649 0.316
ECO_DIFFTRU -0.405 0.080 -0.278
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.90268428 0.014372132 62.8079578
B_HON_NOECO -0.07533303 0.032178010 -2.3411340
PRED_ENV 0.02266067 0.004873954 4.6493396
ECO_DIFFTRUE -0.01135716 0.011562456 -0.9822449
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -177.49 -166.26 94.743 -189.49
full_model 7 -180.21 -167.11 97.103 -194.21 4.7189 1 0.02983 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "8" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 1.14e- 4 1.06 TRUE AW WL
2 0.848 5.55e- 3 1.56 TRUE BT AW
3 0.907 6.70e- 4 1.50 TRUE BT GH
4 0.980 1.01e- 4 1.52 FALSE BT HT
5 0.938 1.02e- 5 2.14 TRUE BT LB
6 0.934 9.45e-11 3.16 FALSE BT MI
7 0.971 1.77e- 4 1.56 FALSE BT NO
8 0.928 2.80e- 3 1.57 TRUE BT RT
9 0.935 8.76e- 3 0.921 FALSE BT WL
10 0.932 1.49e- 4 2.25 TRUE BT ZB
11 0.998 1.48e- 5 1.29 FALSE CB PL
12 0.863 4.06e- 5 0.547 FALSE CB RC
13 0.879 2.96e- 4 1.07 TRUE GH WL
14 0.948 3.92e-12 2.79 TRUE HN CB
15 0.988 0. 2.81 TRUE HN HT
16 0.981 2.51e- 8 2.11 TRUE HT AW
17 0.951 8.88e- 8 2.09 TRUE HT GH
18 0.998 1.47e- 6 2.53 TRUE HT LB
19 0.987 8.29e- 7 2.94 FALSE HT MI
20 0.848 1.00e+ 0 0.0459 FALSE HT NO
21 0.995 0. 2.74 TRUE HT PM
22 0.921 1.60e- 8 2.23 TRUE HT RT
23 0.866 3.73e- 6 1.55 FALSE HT WL
24 0.997 0. 3.39 TRUE HT ZB
25 0.902 1.26e- 7 1.94 FALSE LB CB
26 0.908 8.69e- 6 1.50 TRUE LB MI
27 0.967 0. 4.15 TRUE MI AW
28 0.990 1.06e- 6 2.94 FALSE MI NO
29 0.928 0. 3.38 TRUE MI OK
30 0.977 0. 4.18 TRUE MI RT
31 0.966 0. 3.49 TRUE MI ZB
32 0.840 1.44e- 3 1.12 TRUE RT WL
33 0.946 4.13e- 6 2.06 TRUE SI AD
34 0.960 0. 4.01 TRUE SI AW
35 0.947 6.55e-11 3.18 TRUE SI BT
36 0.950 0. 3.18 TRUE SI CB
37 0.986 0. 3.93 TRUE SI GH
38 0.935 8.94e- 3 0.576 TRUE SI HN
39 0.993 1.00e- 5 2.81 TRUE SI HT
40 0.932 9.72e- 5 1.55 TRUE SI LB
41 0.994 3.21e- 5 2.80 TRUE SI NO
42 0.922 1.75e-12 3.17 TRUE SI OK
43 0.996 0. 3.86 TRUE SI PL
44 0.983 1.05e-10 2.54 TRUE SI PM
45 0.930 1.44e-10 2.94 TRUE SI RC
46 0.976 0. 4.05 TRUE SI RT
47 0.952 0. 3.51 TRUE SI ZB
48 0.987 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-180.2 -167.1 97.1 -194.2 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.3856 -0.4993 -0.0156 0.5616 2.0317
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 4.228e-04 2.056e-02
PORT (Intercept) 7.410e-14 2.722e-07
Residual 7.388e-04 2.718e-02
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.902684 0.014372 62.808
B_HON_NOECO -0.075333 0.032178 -2.341
PRED_ENV 0.022661 0.004874 4.649
ECO_DIFFTRUE -0.011357 0.011562 -0.982
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.347
PRED_ENV -0.649 0.316
ECO_DIFFTRU -0.405 0.080 -0.278
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.90268428 0.014372132 62.8079578
B_HON_NOECO -0.07533303 0.032178010 -2.3411340
PRED_ENV 0.02266067 0.004873954 4.6493396
ECO_DIFFTRUE -0.01135716 0.011562456 -0.9822449
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -177.49 -166.26 94.743 -189.49
full_model 7 -180.21 -167.11 97.103 -194.21 4.7189 1 0.02983 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "9" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.702 1.14e- 4 1.06 TRUE AW WL
2 0.634 5.55e- 3 1.56 TRUE BT AW
3 0.713 6.70e- 4 1.50 TRUE BT GH
4 0.800 1.01e- 4 1.52 FALSE BT HT
5 0.782 1.02e- 5 2.14 TRUE BT LB
6 0.760 9.45e-11 3.16 FALSE BT MI
7 0.801 1.77e- 4 1.56 FALSE BT NO
8 0.728 2.80e- 3 1.57 TRUE BT RT
9 0.728 8.76e- 3 0.921 FALSE BT WL
10 0.764 1.49e- 4 2.25 TRUE BT ZB
11 0.836 1.48e- 5 1.29 FALSE CB PL
12 0.683 4.06e- 5 0.547 FALSE CB RC
13 0.673 2.96e- 4 1.07 TRUE GH WL
14 0.741 3.92e-12 2.79 TRUE HN CB
15 0.840 0. 2.81 TRUE HN HT
16 0.779 2.51e- 8 2.11 TRUE HT AW
17 0.765 8.88e- 8 2.09 TRUE HT GH
18 0.889 1.47e- 6 2.53 TRUE HT LB
19 0.849 8.29e- 7 2.94 FALSE HT MI
20 0.632 1.00e+ 0 0.0459 FALSE HT NO
21 0.838 0. 2.74 TRUE HT PM
22 0.701 1.60e- 8 2.23 TRUE HT RT
23 0.653 3.73e- 6 1.55 FALSE HT WL
24 0.874 0. 3.39 TRUE HT ZB
25 0.735 1.26e- 7 1.94 FALSE LB CB
26 0.721 8.69e- 6 1.50 TRUE LB MI
27 0.745 0. 4.15 TRUE MI AW
28 0.869 1.06e- 6 2.94 FALSE MI NO
29 0.719 0. 3.38 TRUE MI OK
30 0.785 0. 4.18 TRUE MI RT
31 0.790 0. 3.49 TRUE MI ZB
32 0.603 1.44e- 3 1.12 TRUE RT WL
33 0.796 4.13e- 6 2.06 TRUE SI AD
34 0.741 0. 4.01 TRUE SI AW
35 0.743 6.55e-11 3.18 TRUE SI BT
36 0.737 0. 3.18 TRUE SI CB
37 0.797 0. 3.93 TRUE SI GH
38 0.761 8.94e- 3 0.576 TRUE SI HN
39 0.842 1.00e- 5 2.81 TRUE SI HT
40 0.727 9.72e- 5 1.55 TRUE SI LB
41 0.858 3.21e- 5 2.80 TRUE SI NO
42 0.706 1.75e-12 3.17 TRUE SI OK
43 0.841 0. 3.86 TRUE SI PL
44 0.781 1.05e-10 2.54 TRUE SI PM
45 0.699 1.44e-10 2.94 TRUE SI RC
46 0.777 0. 4.05 TRUE SI RT
47 0.777 0. 3.51 TRUE SI ZB
48 0.813 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-140.5 -127.4 77.2 -154.5 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.9523 -0.3791 -0.1032 0.5563 1.9589
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0019538 0.04420
PORT (Intercept) 0.0002001 0.01415
Residual 0.0012038 0.03470
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.721702 0.023949 30.135
B_HON_NOECO -0.166182 0.044067 -3.771
PRED_ENV 0.027017 0.007465 3.619
ECO_DIFFTRUE -0.023079 0.016662 -1.385
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.329
PRED_ENV -0.634 0.363
ECO_DIFFTRU -0.439 0.079 -0.142
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.72170214 0.023949065 30.134877
B_HON_NOECO -0.16618164 0.044067231 -3.771093
PRED_ENV 0.02701726 0.007465388 3.619003
ECO_DIFFTRUE -0.02307903 0.016662276 -1.385107
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.12 -121.90 72.563 -145.12
full_model 7 -140.47 -127.37 77.236 -154.47 9.3471 1 0.002233 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients:
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "10" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.702 1.14e- 4 1.06 TRUE AW WL
2 0.634 5.55e- 3 1.56 TRUE BT AW
3 0.713 6.70e- 4 1.50 TRUE BT GH
4 0.800 1.01e- 4 1.52 FALSE BT HT
5 0.782 1.02e- 5 2.14 TRUE BT LB
6 0.760 9.45e-11 3.16 FALSE BT MI
7 0.801 1.77e- 4 1.56 FALSE BT NO
8 0.728 2.80e- 3 1.57 TRUE BT RT
9 0.728 8.76e- 3 0.921 FALSE BT WL
10 0.764 1.49e- 4 2.25 TRUE BT ZB
11 0.836 1.48e- 5 1.29 FALSE CB PL
12 0.683 4.06e- 5 0.547 FALSE CB RC
13 0.673 2.96e- 4 1.07 TRUE GH WL
14 0.741 3.92e-12 2.79 TRUE HN CB
15 0.840 0. 2.81 TRUE HN HT
16 0.779 2.51e- 8 2.11 TRUE HT AW
17 0.765 8.88e- 8 2.09 TRUE HT GH
18 0.889 1.47e- 6 2.53 TRUE HT LB
19 0.849 8.29e- 7 2.94 FALSE HT MI
20 0.632 1.00e+ 0 0.0459 FALSE HT NO
21 0.838 0. 2.74 TRUE HT PM
22 0.701 1.60e- 8 2.23 TRUE HT RT
23 0.653 3.73e- 6 1.55 FALSE HT WL
24 0.874 0. 3.39 TRUE HT ZB
25 0.735 1.26e- 7 1.94 FALSE LB CB
26 0.721 8.69e- 6 1.50 TRUE LB MI
27 0.745 0. 4.15 TRUE MI AW
28 0.869 1.06e- 6 2.94 FALSE MI NO
29 0.719 0. 3.38 TRUE MI OK
30 0.785 0. 4.18 TRUE MI RT
31 0.790 0. 3.49 TRUE MI ZB
32 0.603 1.44e- 3 1.12 TRUE RT WL
33 0.796 4.13e- 6 2.06 TRUE SI AD
34 0.741 0. 4.01 TRUE SI AW
35 0.743 6.55e-11 3.18 TRUE SI BT
36 0.737 0. 3.18 TRUE SI CB
37 0.797 0. 3.93 TRUE SI GH
38 0.761 8.94e- 3 0.576 TRUE SI HN
39 0.842 1.00e- 5 2.81 TRUE SI HT
40 0.727 9.72e- 5 1.55 TRUE SI LB
41 0.858 3.21e- 5 2.80 TRUE SI NO
42 0.706 1.75e-12 3.17 TRUE SI OK
43 0.841 0. 3.86 TRUE SI PL
44 0.781 1.05e-10 2.54 TRUE SI PM
45 0.699 1.44e-10 2.94 TRUE SI RC
46 0.777 0. 4.05 TRUE SI RT
47 0.777 0. 3.51 TRUE SI ZB
48 0.813 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-140.5 -127.4 77.2 -154.5 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.9523 -0.3791 -0.1032 0.5563 1.9589
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0019538 0.04420
PORT (Intercept) 0.0002001 0.01415
Residual 0.0012038 0.03470
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.721702 0.023949 30.135
B_HON_NOECO -0.166182 0.044067 -3.771
PRED_ENV 0.027017 0.007465 3.619
ECO_DIFFTRUE -0.023079 0.016662 -1.385
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.329
PRED_ENV -0.634 0.363
ECO_DIFFTRU -0.439 0.079 -0.142
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.72170214 0.023949065 30.134877
B_HON_NOECO -0.16618164 0.044067231 -3.771093
PRED_ENV 0.02701726 0.007465388 3.619003
ECO_DIFFTRUE -0.02307903 0.016662276 -1.385107
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -133.12 -121.90 72.563 -145.12
full_model 7 -140.47 -127.37 77.236 -154.47 9.3471 1 0.002233 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "11" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.716 1.14e- 4 1.06 TRUE AW WL
2 0.625 5.55e- 3 1.56 TRUE BT AW
3 0.705 6.70e- 4 1.50 TRUE BT GH
4 0.800 1.01e- 4 1.52 FALSE BT HT
5 0.777 1.02e- 5 2.14 TRUE BT LB
6 0.750 9.45e-11 3.16 FALSE BT MI
7 0.795 1.77e- 4 1.56 FALSE BT NO
8 0.735 2.80e- 3 1.57 TRUE BT RT
9 0.733 8.76e- 3 0.921 FALSE BT WL
10 0.776 1.49e- 4 2.25 TRUE BT ZB
11 0.840 1.48e- 5 1.29 FALSE CB PL
12 0.675 4.06e- 5 0.547 FALSE CB RC
13 0.675 2.96e- 4 1.07 TRUE GH WL
14 0.731 3.92e-12 2.79 TRUE HN CB
15 0.836 0. 2.81 TRUE HN HT
16 0.781 2.51e- 8 2.11 TRUE HT AW
17 0.757 8.88e- 8 2.09 TRUE HT GH
18 0.891 1.47e- 6 2.53 TRUE HT LB
19 0.852 8.29e- 7 2.94 FALSE HT MI
20 0.619 1.00e+ 0 0.0459 FALSE HT NO
21 0.830 0. 2.74 TRUE HT PM
22 0.694 1.60e- 8 2.23 TRUE HT RT
23 0.642 3.73e- 6 1.55 FALSE HT WL
24 0.878 0. 3.39 TRUE HT ZB
25 0.724 1.26e- 7 1.94 FALSE LB CB
26 0.710 8.69e- 6 1.50 TRUE LB MI
27 0.745 0. 4.15 TRUE MI AW
28 0.870 1.06e- 6 2.94 FALSE MI NO
29 0.706 0. 3.38 TRUE MI OK
30 0.793 0. 4.18 TRUE MI RT
31 0.801 0. 3.49 TRUE MI ZB
32 0.607 1.44e- 3 1.12 TRUE RT WL
33 0.805 4.13e- 6 2.06 TRUE SI AD
34 0.734 0. 4.01 TRUE SI AW
35 0.741 6.55e-11 3.18 TRUE SI BT
36 0.736 0. 3.18 TRUE SI CB
37 0.800 0. 3.93 TRUE SI GH
38 0.765 8.94e- 3 0.576 TRUE SI HN
39 0.843 1.00e- 5 2.81 TRUE SI HT
40 0.721 9.72e- 5 1.55 TRUE SI LB
41 0.857 3.21e- 5 2.80 TRUE SI NO
42 0.706 1.75e-12 3.17 TRUE SI OK
43 0.843 0. 3.86 TRUE SI PL
44 0.784 1.05e-10 2.54 TRUE SI PM
45 0.692 1.44e-10 2.94 TRUE SI RC
46 0.777 0. 4.05 TRUE SI RT
47 0.777 0. 3.51 TRUE SI ZB
48 0.830 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-132.8 -119.7 73.4 -146.8 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.91692 -0.43117 -0.08008 0.63312 2.04433
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0019633 0.04431
PORT (Intercept) 0.0001145 0.01070
Residual 0.0015799 0.03975
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.719919 0.024941 28.865
B_HON_NOECO -0.164333 0.049329 -3.331
PRED_ENV 0.026864 0.007967 3.372
ECO_DIFFTRUE -0.022834 0.018292 -1.248
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.330
PRED_ENV -0.636 0.346
ECO_DIFFTRU -0.422 0.072 -0.196
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.71991929 0.024940606 28.865348
B_HON_NOECO -0.16433283 0.049329310 -3.331343
PRED_ENV 0.02686350 0.007967464 3.371650
ECO_DIFFTRUE -0.02283432 0.018291801 -1.248336
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -126.93 -115.7 69.464 -138.93
full_model 7 -132.80 -119.7 73.402 -146.80 7.8757 1 0.00501 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients:
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "12" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.716 1.14e- 4 1.06 TRUE AW WL
2 0.625 5.55e- 3 1.56 TRUE BT AW
3 0.705 6.70e- 4 1.50 TRUE BT GH
4 0.800 1.01e- 4 1.52 FALSE BT HT
5 0.777 1.02e- 5 2.14 TRUE BT LB
6 0.750 9.45e-11 3.16 FALSE BT MI
7 0.795 1.77e- 4 1.56 FALSE BT NO
8 0.735 2.80e- 3 1.57 TRUE BT RT
9 0.733 8.76e- 3 0.921 FALSE BT WL
10 0.776 1.49e- 4 2.25 TRUE BT ZB
11 0.840 1.48e- 5 1.29 FALSE CB PL
12 0.675 4.06e- 5 0.547 FALSE CB RC
13 0.675 2.96e- 4 1.07 TRUE GH WL
14 0.731 3.92e-12 2.79 TRUE HN CB
15 0.836 0. 2.81 TRUE HN HT
16 0.781 2.51e- 8 2.11 TRUE HT AW
17 0.757 8.88e- 8 2.09 TRUE HT GH
18 0.891 1.47e- 6 2.53 TRUE HT LB
19 0.852 8.29e- 7 2.94 FALSE HT MI
20 0.619 1.00e+ 0 0.0459 FALSE HT NO
21 0.830 0. 2.74 TRUE HT PM
22 0.694 1.60e- 8 2.23 TRUE HT RT
23 0.642 3.73e- 6 1.55 FALSE HT WL
24 0.878 0. 3.39 TRUE HT ZB
25 0.724 1.26e- 7 1.94 FALSE LB CB
26 0.710 8.69e- 6 1.50 TRUE LB MI
27 0.745 0. 4.15 TRUE MI AW
28 0.870 1.06e- 6 2.94 FALSE MI NO
29 0.706 0. 3.38 TRUE MI OK
30 0.793 0. 4.18 TRUE MI RT
31 0.801 0. 3.49 TRUE MI ZB
32 0.607 1.44e- 3 1.12 TRUE RT WL
33 0.805 4.13e- 6 2.06 TRUE SI AD
34 0.734 0. 4.01 TRUE SI AW
35 0.741 6.55e-11 3.18 TRUE SI BT
36 0.736 0. 3.18 TRUE SI CB
37 0.800 0. 3.93 TRUE SI GH
38 0.765 8.94e- 3 0.576 TRUE SI HN
39 0.843 1.00e- 5 2.81 TRUE SI HT
40 0.721 9.72e- 5 1.55 TRUE SI LB
41 0.857 3.21e- 5 2.80 TRUE SI NO
42 0.706 1.75e-12 3.17 TRUE SI OK
43 0.843 0. 3.86 TRUE SI PL
44 0.784 1.05e-10 2.54 TRUE SI PM
45 0.692 1.44e-10 2.94 TRUE SI RC
46 0.777 0. 4.05 TRUE SI RT
47 0.777 0. 3.51 TRUE SI ZB
48 0.830 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-132.8 -119.7 73.4 -146.8 41
Scaled residuals:
Min 1Q Median 3Q Max
-1.91692 -0.43117 -0.08008 0.63312 2.04433
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0019633 0.04431
PORT (Intercept) 0.0001145 0.01070
Residual 0.0015799 0.03975
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.719919 0.024941 28.865
B_HON_NOECO -0.164333 0.049329 -3.331
PRED_ENV 0.026864 0.007967 3.372
ECO_DIFFTRUE -0.022834 0.018292 -1.248
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.330
PRED_ENV -0.636 0.346
ECO_DIFFTRU -0.422 0.072 -0.196
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.71991929 0.024940606 28.865348
B_HON_NOECO -0.16433283 0.049329310 -3.331343
PRED_ENV 0.02686350 0.007967464 3.371650
ECO_DIFFTRUE -0.02283432 0.018291801 -1.248336
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -126.93 -115.7 69.464 -138.93
full_model 7 -132.80 -119.7 73.402 -146.80 7.8757 1 0.00501 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "13" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.951 1.14e- 4 1.06 TRUE AW WL
2 0.902 5.55e- 3 1.56 TRUE BT AW
3 0.943 6.70e- 4 1.50 TRUE BT GH
4 0.989 1.01e- 4 1.52 FALSE BT HT
5 0.964 1.02e- 5 2.14 TRUE BT LB
6 0.974 9.45e-11 3.16 FALSE BT MI
7 0.985 1.77e- 4 1.56 FALSE BT NO
8 0.955 2.80e- 3 1.57 TRUE BT RT
9 0.957 8.76e- 3 0.921 FALSE BT WL
10 0.956 1.49e- 4 2.25 TRUE BT ZB
11 1 1.48e- 5 1.29 FALSE CB PL
12 0.907 4.06e- 5 0.547 FALSE CB RC
13 0.913 2.96e- 4 1.07 TRUE GH WL
14 0.980 3.92e-12 2.79 TRUE HN CB
15 0.993 0. 2.81 TRUE HN HT
16 0.988 2.51e- 8 2.11 TRUE HT AW
17 0.969 8.88e- 8 2.09 TRUE HT GH
18 1.00 1.47e- 6 2.53 TRUE HT LB
19 0.994 8.29e- 7 2.94 FALSE HT MI
20 0.891 1.00e+ 0 0.0459 FALSE HT NO
21 0.997 0. 2.74 TRUE HT PM
22 0.948 1.60e- 8 2.23 TRUE HT RT
23 0.905 3.73e- 6 1.55 FALSE HT WL
24 0.998 0. 3.39 TRUE HT ZB
25 0.939 1.26e- 7 1.94 FALSE LB CB
26 0.942 8.69e- 6 1.50 TRUE LB MI
27 0.988 0. 4.15 TRUE MI AW
28 0.998 1.06e- 6 2.94 FALSE MI NO
29 0.962 0. 3.38 TRUE MI OK
30 0.991 0. 4.18 TRUE MI RT
31 0.988 0. 3.49 TRUE MI ZB
32 0.894 1.44e- 3 1.12 TRUE RT WL
33 0.971 4.13e- 6 2.06 TRUE SI AD
34 0.986 0. 4.01 TRUE SI AW
35 0.973 6.55e-11 3.18 TRUE SI BT
36 0.982 0. 3.18 TRUE SI CB
37 0.996 0. 3.93 TRUE SI GH
38 0.958 8.94e- 3 0.576 TRUE SI HN
39 0.997 1.00e- 5 2.81 TRUE SI HT
40 0.966 9.72e- 5 1.55 TRUE SI LB
41 0.998 3.21e- 5 2.80 TRUE SI NO
42 0.959 1.75e-12 3.17 TRUE SI OK
43 0.998 0. 3.86 TRUE SI PL
44 0.996 1.05e-10 2.54 TRUE SI PM
45 0.967 1.44e-10 2.94 TRUE SI RC
46 0.993 0. 4.05 TRUE SI RT
47 0.986 0. 3.51 TRUE SI ZB
48 0.996 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-221.0 -207.9 117.5 -235.0 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.39075 -0.47440 0.00868 0.58801 1.80609
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001525 0.01235
PORT (Intercept) 0.0000000 0.00000
Residual 0.0003291 0.01814
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.933231 0.009376 99.539
B_HON_NOECO -0.054326 0.021335 -2.546
PRED_ENV 0.017866 0.003212 5.562
ECO_DIFFTRUE -0.007158 0.007628 -0.938
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.352
PRED_ENV -0.655 0.312
ECO_DIFFTRU -0.407 0.087 -0.280
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.93323112 0.009375543 99.5388866
B_HON_NOECO -0.05432616 0.021334568 -2.5463913
PRED_ENV 0.01786583 0.003211917 5.5623586
ECO_DIFFTRUE -0.00715808 0.007627890 -0.9384089
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.57 -206.34 114.79 -229.57
full_model 7 -221.04 -207.94 117.52 -235.04 5.4671 1 0.01938 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "14" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.951 1.14e- 4 1.06 TRUE AW WL
2 0.902 5.55e- 3 1.56 TRUE BT AW
3 0.943 6.70e- 4 1.50 TRUE BT GH
4 0.989 1.01e- 4 1.52 FALSE BT HT
5 0.964 1.02e- 5 2.14 TRUE BT LB
6 0.974 9.45e-11 3.16 FALSE BT MI
7 0.985 1.77e- 4 1.56 FALSE BT NO
8 0.955 2.80e- 3 1.57 TRUE BT RT
9 0.957 8.76e- 3 0.921 FALSE BT WL
10 0.956 1.49e- 4 2.25 TRUE BT ZB
11 1 1.48e- 5 1.29 FALSE CB PL
12 0.907 4.06e- 5 0.547 FALSE CB RC
13 0.913 2.96e- 4 1.07 TRUE GH WL
14 0.980 3.92e-12 2.79 TRUE HN CB
15 0.993 0. 2.81 TRUE HN HT
16 0.988 2.51e- 8 2.11 TRUE HT AW
17 0.969 8.88e- 8 2.09 TRUE HT GH
18 1.00 1.47e- 6 2.53 TRUE HT LB
19 0.994 8.29e- 7 2.94 FALSE HT MI
20 0.891 1.00e+ 0 0.0459 FALSE HT NO
21 0.997 0. 2.74 TRUE HT PM
22 0.948 1.60e- 8 2.23 TRUE HT RT
23 0.905 3.73e- 6 1.55 FALSE HT WL
24 0.998 0. 3.39 TRUE HT ZB
25 0.939 1.26e- 7 1.94 FALSE LB CB
26 0.942 8.69e- 6 1.50 TRUE LB MI
27 0.988 0. 4.15 TRUE MI AW
28 0.998 1.06e- 6 2.94 FALSE MI NO
29 0.962 0. 3.38 TRUE MI OK
30 0.991 0. 4.18 TRUE MI RT
31 0.988 0. 3.49 TRUE MI ZB
32 0.894 1.44e- 3 1.12 TRUE RT WL
33 0.971 4.13e- 6 2.06 TRUE SI AD
34 0.986 0. 4.01 TRUE SI AW
35 0.973 6.55e-11 3.18 TRUE SI BT
36 0.982 0. 3.18 TRUE SI CB
37 0.996 0. 3.93 TRUE SI GH
38 0.958 8.94e- 3 0.576 TRUE SI HN
39 0.997 1.00e- 5 2.81 TRUE SI HT
40 0.966 9.72e- 5 1.55 TRUE SI LB
41 0.998 3.21e- 5 2.80 TRUE SI NO
42 0.959 1.75e-12 3.17 TRUE SI OK
43 0.998 0. 3.86 TRUE SI PL
44 0.996 1.05e-10 2.54 TRUE SI PM
45 0.967 1.44e-10 2.94 TRUE SI RC
46 0.993 0. 4.05 TRUE SI RT
47 0.986 0. 3.51 TRUE SI ZB
48 0.996 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-221.0 -207.9 117.5 -235.0 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.39075 -0.47440 0.00868 0.58801 1.80609
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0001525 0.01235
PORT (Intercept) 0.0000000 0.00000
Residual 0.0003291 0.01814
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.933231 0.009376 99.539
B_HON_NOECO -0.054326 0.021335 -2.546
PRED_ENV 0.017866 0.003212 5.562
ECO_DIFFTRUE -0.007158 0.007628 -0.938
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.352
PRED_ENV -0.655 0.312
ECO_DIFFTRU -0.407 0.087 -0.280
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.93323112 0.009375543 99.5388866
B_HON_NOECO -0.05432616 0.021334568 -2.5463913
PRED_ENV 0.01786583 0.003211917 5.5623586
ECO_DIFFTRUE -0.00715808 0.007627890 -0.9384089
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -217.57 -206.34 114.79 -229.57
full_model 7 -221.04 -207.94 117.52 -235.04 5.4671 1 0.01938 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸
Starting new analysis, with data index DIDX "15" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 64 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 64 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 1.14e- 4 1.06 TRUE AW WL
2 0.847 5.55e- 3 1.56 TRUE BT AW
3 0.906 6.70e- 4 1.50 TRUE BT GH
4 0.980 1.01e- 4 1.52 FALSE BT HT
5 0.936 1.02e- 5 2.14 TRUE BT LB
6 0.933 9.45e-11 3.16 FALSE BT MI
7 0.971 1.77e- 4 1.56 FALSE BT NO
8 0.927 2.80e- 3 1.57 TRUE BT RT
9 0.934 8.76e- 3 0.921 FALSE BT WL
10 0.931 1.49e- 4 2.25 TRUE BT ZB
11 0.998 1.48e- 5 1.29 FALSE CB PL
12 0.863 4.06e- 5 0.547 FALSE CB RC
13 0.879 2.96e- 4 1.07 TRUE GH WL
14 0.947 3.92e-12 2.79 TRUE HN CB
15 0.988 0. 2.81 TRUE HN HT
16 0.981 2.51e- 8 2.11 TRUE HT AW
17 0.951 8.88e- 8 2.09 TRUE HT GH
18 0.998 1.47e- 6 2.53 TRUE HT LB
19 0.987 8.29e- 7 2.94 FALSE HT MI
20 0.848 1.00e+ 0 0.0459 FALSE HT NO
21 0.995 0. 2.74 TRUE HT PM
22 0.920 1.60e- 8 2.23 TRUE HT RT
23 0.865 3.73e- 6 1.55 FALSE HT WL
24 0.997 0. 3.39 TRUE HT ZB
25 0.901 1.26e- 7 1.94 FALSE LB CB
26 0.907 8.69e- 6 1.50 TRUE LB MI
27 0.966 0. 4.15 TRUE MI AW
28 0.990 1.06e- 6 2.94 FALSE MI NO
29 0.927 0. 3.38 TRUE MI OK
30 0.976 0. 4.18 TRUE MI RT
31 0.965 0. 3.49 TRUE MI ZB
32 0.839 1.44e- 3 1.12 TRUE RT WL
33 0.945 4.13e- 6 2.06 TRUE SI AD
34 0.960 0. 4.01 TRUE SI AW
35 0.947 6.55e-11 3.18 TRUE SI BT
36 0.950 0. 3.18 TRUE SI CB
37 0.986 0. 3.93 TRUE SI GH
38 0.935 8.94e- 3 0.576 TRUE SI HN
39 0.994 1.00e- 5 2.81 TRUE SI HT
40 0.931 9.72e- 5 1.55 TRUE SI LB
41 0.995 3.21e- 5 2.80 TRUE SI NO
42 0.919 1.75e-12 3.17 TRUE SI OK
43 0.997 0. 3.86 TRUE SI PL
44 0.983 1.05e-10 2.54 TRUE SI PM
45 0.928 1.44e-10 2.94 TRUE SI RC
46 0.976 0. 4.05 TRUE SI RT
47 0.952 0. 3.51 TRUE SI ZB
48 0.987 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-179.1 -166.0 96.5 -193.1 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.37300 -0.53155 -0.01222 0.56566 2.04362
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0004388 0.02095
PORT (Intercept) 0.0000000 0.00000
Residual 0.0007533 0.02745
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.901945 0.014542 62.025
B_HON_NOECO -0.075597 0.032511 -2.325
PRED_ENV 0.022821 0.004927 4.632
ECO_DIFFTRUE -0.011451 0.011687 -0.980
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.346
PRED_ENV -0.649 0.316
ECO_DIFFTRU -0.405 0.079 -0.278
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.90194454 0.014541696 62.024712
B_HON_NOECO -0.07559749 0.032511184 -2.325276
PRED_ENV 0.02282126 0.004926948 4.631925
ECO_DIFFTRUE -0.01145129 0.011687114 -0.979822
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -176.43 -165.20 94.215 -188.43
full_model 7 -179.09 -165.99 96.546 -193.09 4.6622 1 0.03083 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
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Starting new analysis, with data index DIDX "16" and formula index FIDX "4" in Summary Tables.Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_hon_info.csv.
Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.- Setting types.- Input dimensions are: 69 17 .- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .- Intermediate dimensions are: 69 6 .- Undefined rows have been removed, assuming they were real "NA" and not "0".- Final dimensions are: 48 6 .# A tibble: 48 x 6
RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST
<dbl> <dbl> <dbl> <fct> <fct> <fct>
1 0.919 1.14e- 4 1.06 TRUE AW WL
2 0.847 5.55e- 3 1.56 TRUE BT AW
3 0.906 6.70e- 4 1.50 TRUE BT GH
4 0.980 1.01e- 4 1.52 FALSE BT HT
5 0.936 1.02e- 5 2.14 TRUE BT LB
6 0.933 9.45e-11 3.16 FALSE BT MI
7 0.971 1.77e- 4 1.56 FALSE BT NO
8 0.927 2.80e- 3 1.57 TRUE BT RT
9 0.934 8.76e- 3 0.921 FALSE BT WL
10 0.931 1.49e- 4 2.25 TRUE BT ZB
11 0.998 1.48e- 5 1.29 FALSE CB PL
12 0.863 4.06e- 5 0.547 FALSE CB RC
13 0.879 2.96e- 4 1.07 TRUE GH WL
14 0.947 3.92e-12 2.79 TRUE HN CB
15 0.988 0. 2.81 TRUE HN HT
16 0.981 2.51e- 8 2.11 TRUE HT AW
17 0.951 8.88e- 8 2.09 TRUE HT GH
18 0.998 1.47e- 6 2.53 TRUE HT LB
19 0.987 8.29e- 7 2.94 FALSE HT MI
20 0.848 1.00e+ 0 0.0459 FALSE HT NO
21 0.995 0. 2.74 TRUE HT PM
22 0.920 1.60e- 8 2.23 TRUE HT RT
23 0.865 3.73e- 6 1.55 FALSE HT WL
24 0.997 0. 3.39 TRUE HT ZB
25 0.901 1.26e- 7 1.94 FALSE LB CB
26 0.907 8.69e- 6 1.50 TRUE LB MI
27 0.966 0. 4.15 TRUE MI AW
28 0.990 1.06e- 6 2.94 FALSE MI NO
29 0.927 0. 3.38 TRUE MI OK
30 0.976 0. 4.18 TRUE MI RT
31 0.965 0. 3.49 TRUE MI ZB
32 0.839 1.44e- 3 1.12 TRUE RT WL
33 0.945 4.13e- 6 2.06 TRUE SI AD
34 0.960 0. 4.01 TRUE SI AW
35 0.947 6.55e-11 3.18 TRUE SI BT
36 0.950 0. 3.18 TRUE SI CB
37 0.986 0. 3.93 TRUE SI GH
38 0.935 8.94e- 3 0.576 TRUE SI HN
39 0.994 1.00e- 5 2.81 TRUE SI HT
40 0.931 9.72e- 5 1.55 TRUE SI LB
41 0.995 3.21e- 5 2.80 TRUE SI NO
42 0.919 1.75e-12 3.17 TRUE SI OK
43 0.997 0. 3.86 TRUE SI PL
44 0.983 1.05e-10 2.54 TRUE SI PM
45 0.928 1.44e-10 2.94 TRUE SI RC
46 0.976 0. 4.05 TRUE SI RT
47 0.952 0. 3.51 TRUE SI ZB
48 0.987 4.24e- 7 2.77 TRUE WL ZB
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST . ... dimensions: 48 6 . ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .boundary (singular) fit: see ?isSingular
Getting Model Summary: Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
Data: data_item
AIC BIC logLik deviance df.resid
-179.1 -166.0 96.5 -193.1 41
Scaled residuals:
Min 1Q Median 3Q Max
-2.37300 -0.53155 -0.01222 0.56566 2.04362
Random effects:
Groups Name Variance Std.Dev.
DEST (Intercept) 0.0004388 0.02095
PORT (Intercept) 0.0000000 0.00000
Residual 0.0007533 0.02745
Number of obs: 48, groups: DEST, 17; PORT, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.901945 0.014542 62.025
B_HON_NOECO -0.075597 0.032511 -2.325
PRED_ENV 0.022821 0.004927 4.632
ECO_DIFFTRUE -0.011451 0.011687 -0.980
Correlation of Fixed Effects:
(Intr) B_HON_ PRED_E
B_HON_NOECO -0.346
PRED_ENV -0.649 0.316
ECO_DIFFTRU -0.405 0.079 -0.278
convergence code: 0
boundary (singular) fit: see ?isSingular
Getting Model Coefficients from Summary: Estimate Std. Error t value
(Intercept) 0.90194454 0.014541696 62.024712
B_HON_NOECO -0.07559749 0.032511184 -2.325276
PRED_ENV 0.02282126 0.004926948 4.631925
ECO_DIFFTRUE -0.01145129 0.011687114 -0.979822
Getting Model ANOVA: Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +
full_model: (1 | DEST)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model 6 -176.43 -165.20 94.215 -188.43
full_model 7 -179.09 -165.99 96.546 -193.09 4.6622 1 0.03083 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plotting Model Coefficients: boundary (singular) fit: see ?isSingular
Show Results table
Check above raw model out put for Writing above results to results table row: n and look up n in both tables below.
Sort results table by AIC
analysis_summaries <- arrange(analysis_summaries, AKAI)Show results table interactively:
analysis_summariesShow results table on screen:
print(analysis_summaries, n = Inf)# A tibble: 64 x 6
DIDX FIDX AKAI PVAL FRML DATA
<int> <int> <dbl> <dbl> <chr> <chr>
1 6 1 -305. 0.186 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_…
2 14 1 -304. 0.190 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_…
3 5 1 -279. 0.191 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph…
4 13 1 -279. 0.197 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph…
5 8 1 -245. 0.247 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_…
6 16 1 -244. 0.252 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_…
7 7 1 -226. 0.272 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph…
8 15 1 -224. 0.281 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph…
9 5 4 -221. 0.0187 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph…
10 6 4 -221. 0.0187 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_…
11 13 4 -221. 0.0194 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph…
12 14 4 -221. 0.0194 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_…
13 5 2 -220. 0.0398 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph…
14 6 2 -220. 0.0398 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_…
15 13 2 -220. 0.0412 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph…
16 14 2 -220. 0.0412 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_…
17 5 3 -216. 0.557 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph…
18 6 3 -216. 0.557 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_…
19 13 3 -216. 0.548 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph…
20 14 3 -216. 0.548 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_…
21 7 4 -180. 0.0298 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph…
22 8 4 -180. 0.0298 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_…
23 15 4 -179. 0.0308 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph…
24 16 4 -179. 0.0308 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_…
25 7 2 -179. 0.0592 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph…
26 8 2 -179. 0.0592 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_…
27 15 2 -178. 0.0610 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph…
28 16 2 -178. 0.0610 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_…
29 10 1 -178. 0.0355 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_…
30 7 3 -176. 0.520 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph…
31 8 3 -176. 0.520 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_…
32 2 1 -175. 0.0431 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_…
33 15 3 -175. 0.526 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph…
34 16 3 -175. 0.526 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_…
35 4 1 -175. 0.0474 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_…
36 12 1 -169. 0.0457 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_…
37 9 1 -161. 0.0512 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph…
38 1 1 -159. 0.0617 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph…
39 3 1 -159. 0.0676 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph…
40 11 1 -154. 0.0660 RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph…
41 9 4 -140. 0.00223 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph…
42 10 4 -140. 0.00223 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_…
43 1 4 -140. 0.00266 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph…
44 2 4 -140. 0.00266 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_…
45 9 2 -140. 0.00372 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph…
46 10 2 -140. 0.00372 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_…
47 2 2 -139. 0.00434 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_…
48 1 2 -139. 0.00434 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph…
49 3 4 -138. 0.00426 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph…
50 4 4 -138. 0.00426 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_…
51 3 2 -137. 0.00769 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph…
52 4 2 -137. 0.00769 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_…
53 11 4 -133. 0.00501 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph…
54 12 4 -133. 0.00501 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_…
55 9 3 -132. 0.299 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph…
56 10 3 -132. 0.299 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_…
57 2 3 -132. 0.336 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_…
58 1 3 -132. 0.336 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph…
59 11 2 -132. 0.00847 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph…
60 12 2 -132. 0.00847 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_…
61 3 3 -130. 0.388 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph…
62 4 3 -130. 0.388 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_…
63 11 3 -126. 0.349 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph…
64 12 3 -126. 0.349 RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_…On warning ?isSingular
Complex mixed-effect models (i.e., those with a large number of variance-covariance parameters) frequently result in singular fits, i.e. estimated variance-covariance matrices with less than full rank. Less technically, this means that some “dimensions” of the variance-covariance matrix have been estimated as exactly zero. For scalar random effects such as intercept-only models, or 2-dimensional random effects such as intercept+slope models, singularity is relatively easy to detect because it leads to random-effect variance estimates of (nearly) zero, or estimates of correlations that are (almost) exactly -1 or 1. However, for more complex models (variance-covariance matrices of dimension >=3) singularity can be hard to detect; models can often be singular without any of their individual variances being close to zero or correlations being close to +/-1.
This function performs a simple test to determine whether any of the random effects covariance matrices of a fitted model are singular. The rePCA method provides more detail about the singularity pattern, showing the standard deviations of orthogonal variance components and the mapping from variance terms in the model to orthogonal components (i.e., eigenvector/rotation matrices).
While singular models are statistically well defined (it is theoretically sensible for the true maximum likelihood estimate to correspond to a singular fit), there are real concerns that (1) singular fits correspond to overfitted models that may have poor power; (2) chances of numerical problems and mis-convergence are higher for singular models (e.g. it may be computationally difficult to compute profile confidence intervals for such models); (3) standard inferential procedures such as Wald statistics and likelihood ratio tests may be inappropriate.
There is not yet consensus about how to deal with singularity, or more generally to choose which random-effects specification (from a range of choices of varying complexity) to use. Some proposals include:
avoid fitting overly complex models in the first place, i.e. design experiments/restrict models a priori such that the variance-covariance matrices can be estimated precisely enough to avoid singularity (Matuschek et al 2017)
use some form of model selection to choose a model that balances predictive accuracy and overfitting/type I error (Bates et al 2015, Matuschek et al 2017)
“keep it maximal”, i.e. fit the most complex model consistent with the experimental design, removing only terms required to allow a non-singular fit (Barr et al. 2013), or removing further terms based on p-values or AIC
use a partially Bayesian method that produces maximum a posteriori (MAP) estimates using regularizing priors to force the estimated random-effects variance-covariance matrices away from singularity (Chung et al 2013, blme package)
use a fully Bayesian method that both regularizes the model via informative priors and gives estimates and credible intervals for all parameters that average over the uncertainty in the random effects parameters (Gelman and Hill 2006, McElreath 2015; MCMCglmm, rstanarm and brms packages) # Session info
The code and output in this document were tested and generated in the following computing environment:
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_1.5 formula.tools_1.7.1 cowplot_1.0.0 sjPlot_2.8.2 lme4_1.1-21 Matrix_1.2-18 reshape2_1.4.3
[8] gdata_2.18.0 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3 readr_1.3.1 tidyr_1.0.2
[15] tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] nlme_3.1-143 fs_1.3.1 lubridate_1.7.4 RColorBrewer_1.1-2 insight_0.8.0 httr_1.4.1 tools_3.6.1
[8] backports_1.1.5 utf8_1.1.4 R6_2.4.1 sjlabelled_1.1.3 DBI_1.1.0 lazyeval_0.2.2 colorspace_1.4-1
[15] withr_2.1.2 tidyselect_1.0.0 emmeans_1.4.4 compiler_3.6.1 performance_0.4.3 cli_2.0.1 rvest_0.3.5
[22] xml2_1.2.2 sandwich_2.5-1 bayestestR_0.5.1 scales_1.1.0 mvtnorm_1.0-12 digest_0.6.23 minqa_1.2.4
[29] rmarkdown_2.1 base64enc_0.1-3 pkgconfig_2.0.3 htmltools_0.4.0 dbplyr_1.4.2 highr_0.8 rlang_0.4.4
[36] readxl_1.3.1 rstudioapi_0.10 farver_2.0.3 generics_0.0.2 zoo_1.8-7 jsonlite_1.6 gtools_3.8.1
[43] parameters_0.4.1 Rcpp_1.0.3 munsell_0.5.0 fansi_0.4.1 lifecycle_0.1.0 stringi_1.4.5 multcomp_1.4-12
[50] yaml_2.2.0 snakecase_0.11.0 MASS_7.3-51.5 plyr_1.8.5 grid_3.6.1 sjmisc_2.8.3 crayon_1.3.4
[57] lattice_0.20-38 ggeffects_0.14.1 haven_2.2.0 splines_3.6.1 sjstats_0.17.8 hms_0.5.3 knitr_1.27
[64] pillar_1.4.3 boot_1.3-24 estimability_1.3 effectsize_0.1.1 codetools_0.2-16 reprex_0.3.0 glue_1.3.1
[71] evaluate_0.14 modelr_0.1.5 operator.tools_1.6.3 vctrs_0.2.2 nloptr_1.2.1 cellranger_1.1.0 gtable_0.3.0
[78] assertthat_0.2.1 xfun_0.12 xtable_1.8-4 broom_0.5.4 coda_0.19-3 survival_3.1-8 TH.data_1.0-10 References
#' ---
#' title: "Apply Mixed Effect Models to Extended Modelling Input Data"
#' output: 
#'   html_document:
#'   toc: true
#'   toc_float: true
#'   toc_collapsed: true
#' toc_depth: 3
#' number_sections: true
#' theme: lumen
#' ---

#' # Preamble
#' 
#' This code commentary is included in the R code itself and can be rendered at
#' any stage using `rmarkdown::render ("/Users/paul/Documents/CU_combined/Github/500_83_get_mixed_effect_model_results.R", clean = TRUE, output_format = "html_notebook")`.
#' Please check the session info at the end of the document for further 
#' notes on the coding environment.
#' 
#' # Environment preparation
#'
#' Empty buffer.

rm(list=ls())

#' Load Packages

library ("tidyverse") # dplyr and friends
library ("ggplot2")   # for ggCaterpillar
library ("gdata")     # matrix functions
library ("reshape2")  # melting
library ("lme4")      # mixed effect model
library ("sjPlot")    # mixed effect model - with plotting
library ("cowplot")   # exporting ggplots
library ("formula.tools") # better formatting of formulas
library ("stringr")    # better string concatenation
library ("magrittr")  # back-piping (only used for type conversion)
#' Functions

# Loaded from helper script:
source("/Users/paul/Documents/CU_combined/Github/500_00_functions.R")

#' "Not in" function
`%!in%` = Negate(`%in%`)

#' Function to subset data to fit model variables. Currently there are more 
#' incomplete cases among Notre-Dame predictors then among Cornell predictors.
#' Consider running an extra analysis \n with Cornell data trimmed so as to match Notre Dame data.
match_data_to_formula <- function (formula_item, data_item){
  
  # package loading
  require ("tidyverse")
  
  # message
  message("\nData is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.")
  
  # Setting types
  #   for debugging only
  # print(head(data_item))
  
  message("- Setting types.")
  cols <- c("PORT", "DEST", "ECO_PORT", "ECO_DEST", "ECO_DIFF")
  data_item[cols] <- lapply(data_item[cols], as.factor)  

  #   for debugging only
  # print(head(data_item))
  
        
  # remove superflous columns
  vars_to_keep <- all.vars (formula_item)

  message("- Input dimensions are: ", paste0( (dim(data_item)), " "),  ".")
  message("- Removed variables are: ", paste0( names(data_item)[which(names(data_item) %!in% vars_to_keep)], " "), ".")
  message("- Kept variables are: ", paste0(vars_to_keep, " "), ".")
  
  data_item <- data_item %>% select(all_of(vars_to_keep))

  message("- Intermediate dimensions are: ", paste0( (dim(data_item)), " "), ".")
  
  # remove superflous rows
  message("- Undefined rows have been removed, assuming they were real \"NA\" and not \"0\".")
  
  data_item <- data_item %>% filter(complete.cases(.))
  
  message("- Final dimensions are: ", paste0( (dim(data_item)), " "), ".")
  
  # return table object suitable for modelling with model formula
  return(data_item)

}

#' Calculate random effect model results
calculate_model <- function(formula_item, data_item){
  
  message("\nModelling function received variables: ", paste0(names(data_item) , " "), ".")
  message("   ... dimensions: ", paste0( (dim(data_item)), " "), ".")
  message("   ... formula: ", paste0(formula_item , " "), "." )
  
  model <- lmer(formula_item, data = data_item, REML=FALSE)

  return(model)
}

#' # Model definitions
#' 
#' ##  Define full models
#'
#' following `https://stackoverflow.com/questions/25312818/using-lapply-to-fit-multiple-model-how-to-keep-the-model-formula-self-contain`

full_formulae <- list(
  
  # Original by Paul 
  as.formula(RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # as per email 04.02.2020
  # Unifrac ~ VOY_FREQ + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
   
  # Unifrac ~ B_FON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # Unifrac ~ B_HON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST))
)

#' 
#' ##  Define null models
#'
#' For Anova comparison. Order *must* be the same as in list `full_models`.

null_formulae <- list(
  
  # Original by Paul 
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # as per email 04.02.2020
  # Unifrac ~ VOY_FREQ + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
   
  # Unifrac ~ B_FON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # Unifrac ~ B_HON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST))
)

#' # Read in and format data
#'
#' Please refer to project README.md file for further details on previous processing steps (dated 31-Jan-2020). 

# define file path components for listing 
model_input_folder <- "/Users/paul/Documents/CU_combined/Zenodo/Results"
model_input_pattern <- glob2rx("??_results_euk_*_model_data_*.csv")

# read all file into lists for `lapply()` usage
model_input_files <- list.files(path=model_input_folder, 
  pattern = model_input_pattern, full.names = TRUE)

# store all tables in list and save input filenames alongside - skipping "X1" 
#  in case previous tables have column numbers, which they should not have anymore.
model_input_data <- suppressWarnings(lapply(model_input_files, 
  function(listed_file)  read_csv(listed_file, col_types = cols('X1' = col_skip()))))
names(model_input_data) <- model_input_files

#' # Obtaining modelling results
#'
#' ## Initialize results table
#' 
#' So that it can be filled in the loop.

analysis_summaries <- expand.grid(seq(model_input_data), seq(full_formulae))
analysis_summaries <- as_tibble(analysis_summaries)
analysis_summaries <- setNames(analysis_summaries, c("DIDX", "FIDX"))
analysis_summaries <- analysis_summaries %>% add_column(AKAI = 0, PVAL = 0, FRML = 0, DATA = 0)

analysis_summaries$AKAI  %<>% as.double
analysis_summaries$DATA %<>% as.character
analysis_summaries$FRML  %<>% as.character
analysis_summaries$PVAL  %<>% as.double

# use this approach to get around the loop - later
#   define all possible combinations for mapply call
#   for later - starting point
#   analysis_combinations <- expand.grid(seq(model_input_data), seq(full_formulae))
#   setNames(analysis_combinations, c("model_index", "formula_index"))
#   for later - starting point
#   list(model_input_data, full_formulae)

#'
#' ## Calculating Results
#' 
#' Initially using loops, for sanity reasons. While looping fill results table
#' `analysis_summaries`. 
#' Check raw model outputs below for `Writing above results to results table row: n` and look up `n` in both results tables all the way at the end of this page.

# loop over formulae
for (i in seq(full_formulae)){
  
  # loop over dat sets
  for (j in seq(model_input_data)){
  
    message("°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ ")
    message("\nStarting new analysis, with data index DIDX \"", j , "\" and formula index FIDX \"", i, "\" in Summary Tables." ) 
    message("Using formula: ", as.character(full_formulae[[i]]), " with data: ", as.character(basename(names(model_input_data)[[j]])), ". ")

    # define current model formula for parsing
    full_formula <- full_formulae[[i]]
    null_formula <- null_formulae[[i]]
     
    # define current data table for subsetting
    model_data_raw <- model_input_data[[j]]
         
    # match input table dimensions to current model formulae
    model_data <- match_data_to_formula(full_formula, model_data_raw)
    print(model_data, n = Inf)
  
    # calculate full model
    full_model <- calculate_model(full_formula, model_data)
     
    # calculate null model
    null_model <- calculate_model(null_formula, model_data)
     
    # print model summary and evaluations
    message("\nGetting Model Summary: ")
    sm <- summary(full_model)
    print(sm)
    message("\nGetting Model Coefficients from Summary: ")
    print(sm$coefficients)
      
    message("\nGetting Model ANOVA: ")
    an <- try(anova(null_model, full_model))
    try(print(an))

    # plot model coefficients
    message("\nPlotting Model Coefficients: ")
    plot <- plot_model(full_model, show.values = TRUE, value.offset = .3,
     type = "std", 
     title = paste("Coefficients for formula \"", as.character(full_formula),
     "\" and variables \"", str_c(names(model_data), collapse = "\", \""),"\" of input file: \"",
    basename(names(model_input_data)[[j]]), "\"." ))
  
    print(plot)
  
    # gather results
    #   set current row of results table
    crnt_row <- intersect(which(analysis_summaries$DIDX == j), which(analysis_summaries$FIDX == i))
    # message("Writing above results to results table row (but the table is re-sorted): ", crnt_row)
  
    #    fill results table
    analysis_summaries[crnt_row, ]$AKAI <- extractAIC(full_model)[2]
    analysis_summaries[crnt_row, ]$DATA <- as.character(basename(names(model_input_data)[[j]]))
    analysis_summaries[crnt_row, ]$FRML <- as.character(full_formulae[[i]])
    analysis_summaries[crnt_row, ]$PVAL <- an[2,8]
  
    # keep in mind for further elements from anova object:
    #  > str(an)
    #  Classes ‘anova’ and 'data.frame':	2 obs. of  8 variables:
    #  $ Df        : num  6 7
    #  $ AIC       : num  -158 -159
    #  $ BIC       : num  -145 -144
    #  $ logLik    : num  84.8 86.5
    #  $ deviance  : num  -170 -173
    #  $ Chisq     : num  NA 3.49
    #  $ Chi Df    : num  NA 1
    #  $ Pr(>Chisq): num  NA 0.0617

  }
}

#' # Show Results table
#'
#' Check above raw model out put for `Writing above results to results table row: n` and look up `n` in both tables below.

#'
#' Sort results table by AIC

analysis_summaries <- arrange(analysis_summaries, AKAI)

#' Show results table interactively:

analysis_summaries

#' Show results table on screen:

print(analysis_summaries, n = Inf)

#' # On warning ?`isSingular`
#' 
#'  Complex mixed-effect models (i.e., those with
#' a large number of variance-covariance
#' parameters) frequently result in singular fits,
#' i.e. estimated variance-covariance matrices
#' with less than full rank. Less technically,
#' this means that some "dimensions" of the
#' variance-covariance matrix have been estimated
#' as exactly zero. For scalar random effects such
#' as intercept-only models, or 2-dimensional
#' random effects such as intercept+slope models,
#' singularity is relatively easy to detect
#' because it leads to random-effect variance
#' estimates of (nearly) zero, or estimates of
#' correlations that are (almost) exactly -1 or 1.
#' However, for more complex models
#' (variance-covariance matrices of dimension >=3)
#' singularity can be hard to detect; models can
#' often be singular without any of their
#' individual variances being close to zero or
#' correlations being close to +/-1.
#' 
#'   This function performs a simple test to
#' determine whether any of the random effects
#' covariance matrices of a fitted model are
#' singular. The rePCA method provides more detail
#' about the singularity pattern, showing the
#' standard deviations of orthogonal variance
#' components and the mapping from variance terms
#' in the model to orthogonal components (i.e.,
#' eigenvector/rotation matrices).
#' 
#'   While singular models are statistically well
#' defined (it is theoretically sensible for the
#' true maximum likelihood estimate to correspond
#' to a singular fit), there are real concerns
#' that (1) singular fits correspond to overfitted
#' models that may have poor power; (2) chances of
#' numerical problems and mis-convergence are
#' higher for singular models (e.g. it may be
#' computationally difficult to compute profile
#' confidence intervals for such models); (3)
#' standard inferential procedures such as Wald
#' statistics and likelihood ratio tests may be
#' inappropriate.
#' 
#'   There is not yet consensus about how to deal
#' with singularity, or more generally to choose
#' which random-effects specification (from a
#' range of choices of varying complexity) to use.
#' Some proposals include:
#' 
#'   avoid fitting overly complex models in the
#' first place, i.e. design experiments/restrict
#' models a priori such that the
#' variance-covariance matrices can be estimated
#' precisely enough to avoid singularity
#' (Matuschek et al 2017)
#' 
#'   use some form of model selection to choose a
#' model that balances predictive accuracy and
#' overfitting/type I error (Bates et al 2015,
#' Matuschek et al 2017)
#' 
#'   “keep it maximal”, i.e. fit the most complex
#' model consistent with the experimental design,
#' removing only terms required to allow a
#' non-singular fit (Barr et al. 2013), or
#' removing further terms based on p-values or AIC
#' 
#'   use a partially Bayesian method that produces
#' maximum a posteriori (MAP) estimates using
#' regularizing priors to force the estimated
#' random-effects variance-covariance matrices
#' away from singularity (Chung et al 2013, blme
#' package)
#' 
#'   use a fully Bayesian method that both
#' regularizes the model via informative priors
#' and gives estimates and credible intervals for
#' all parameters that average over the
#' uncertainty in the random effects parameters
#' (Gelman and Hill 2006, McElreath 2015;
#' MCMCglmm, rstanarm and brms packages)

#' # Session info
#'
#' The code and output in this document were tested and generated in the
#' following computing environment:
#+ echo=FALSE
sessionInfo()

#' # References
